GCL Flashcards
You are migrating workloads to the cloud. The goal of the migration is to serve customers worldwide as quickly as possible According to local regulations, certain data is required to be stored in a specific geographic area, and it can be served worldwide. You need to design the architecture and deployment for your workloads.
What should you do?
A. Select a public cloud provider that is only active in the required geographic area
B. Select a private cloud provider that globally replicates data storage for fast data access
C. Select a public cloud provider that guarantees data location in the required geographic area
D. Select a private cloud provider that is only active in the required geographic area
To serve customers worldwide while adhering to local regulations regarding data storage in specific geographic areas, the most suitable option would be:
C. Select a public cloud provider that guarantees data location in the required geographic area.
Explanation:
- Public Cloud Provider: Using a public cloud provider allows for global reach, enabling you to serve customers worldwide efficiently.
- Guarantees Data Location in Required Geographic Area: This option ensures that the chosen public cloud provider guarantees data storage in the specific geographic area as required by local regulations. This ensures compliance with data residency and sovereignty requirements.
By choosing a public cloud provider that ensures data location in the required geographic area, you can achieve a balance between global reach for your services and compliance with local data storage regulations.
Your organization needs a large amount of extra computing power within the next two weeks.
After those two weeks, the need for the additional resources will end.
Which is the most cost-effective approach?
A. Use a committed use discount to reserve a very powerful virtual machine
B. Purchase one very powerful physical computer
C. Start a very powerful virtual machine without using a committed use discount
D. Purchase multiple physical computers and scale workload across them
For a short-term need of extra computing power within the next two weeks, the most cost-effective approach would typically be:
C. Start a very powerful virtual machine without using a committed use discount.
Explanation:
- Very Powerful Virtual Machine: Opting for a powerful virtual machine is efficient for short-term, high-compute needs. Virtual machines can be provisioned quickly and scaled up or down based on demand, making them a flexible choice for temporary requirements.
- No Committed Use Discount: Since the need for additional resources is only for a short period (two weeks), committing to a longer-term usage with a discount (as in option A) may not be the most cost-effective approach, as it might lead to underutilization and unnecessary costs once the two-week requirement ends.
Purchasing physical computers (option B) or multiple physical computers (option D) may be costly and time-consuming, and the resources may go underutilized after the short-term need ends, making them less cost-effective for this scenario.
In summary, starting a powerful virtual machine without committing to a long-term contract or discount is likely the most cost-effective approach given the short-term nature of the computing power need.
Your organization needs to plan its cloud infrastructure expenditures.
Which should your organization do?
A. Review cloud resource costs frequently, because costs change often based on use
B. Review cloud resource costs annually as part of planning your organization’s overall budget
C. If your organization uses only cloud resources, infrastructure costs are no longer part of your overall budget
D. Involve fewer people in cloud resource planning than your organization did for on-premises resource planning
When planning cloud infrastructure expenditures for an organization, it’s important to adopt practices that promote effective cost management and budget planning. The most appropriate approach is:
B. Review cloud resource costs annually as part of planning your organization’s overall budget.
Explanation:
- Annual Review of Costs: Regularly reviewing cloud resource costs on an annual basis aligns with the organization’s budget planning cycle. It allows for a comprehensive assessment of cloud spending and helps in making informed decisions regarding budget allocation and resource utilization.
- Part of Overall Budgeting: Cloud infrastructure costs should indeed be integrated into the organization’s overall budgeting process. Understanding the costs associated with cloud resources helps in allocating appropriate funds and ensuring financial accountability.
Option A (frequent review of cloud resource costs) can also be beneficial for monitoring costs, especially when there are significant usage fluctuations. However, an annual review is necessary to align with broader budgeting processes and strategies.
Option C is incorrect as cloud resource costs should definitely be part of the overall budget, even if the organization primarily uses cloud resources.
Option D is not recommended. Involving the right people in cloud resource planning is crucial to ensure that the planning is comprehensive, efficient, and aligns with organizational goals and requirements. It’s important to have a cross-functional team involved to gather diverse perspectives and insights into resource needs and cost implications.
The operating systems of some of your organization’s virtual machines may have a security vulnerability.
How can your organization most effectively identify all virtual machines that do not have the latest security update?
A. View the Security Command Center to identify virtual machines running vulnerable disk images
B. View the Compliance Reports Manager to identify and download a recent PCI audit
C. View the Security Command Center to identify virtual machines started more than 2 weeks ago
D. View the Compliance Reports Manager to identify and download a recent SOC 1 audit
To effectively identify all virtual machines that do not have the latest security update, the most appropriate option is:
A. View the Security Command Center to identify virtual machines running vulnerable disk images.
Explanation:
- Security Command Center: The Security Command Center is a tool that provides centralized visibility into security information and vulnerabilities across your infrastructure. In this context, using the Security Command Center to identify virtual machines running vulnerable disk images is a logical choice for identifying those that may not have the latest security updates.
- Identifying Vulnerable Disk Images: By using the Security Command Center, you can scan and identify virtual machines running disk images with known vulnerabilities. This allows your organization to prioritize updating those virtual machines and ensuring they have the latest security updates.
Options B and D involve Compliance Reports Manager and audits related to compliance (PCI and SOC 1). While compliance audits are important for regulatory adherence, they may not directly address identifying specific vulnerabilities or outdated security updates on virtual machines.
Option C, viewing virtual machines started more than 2 weeks ago, is not a direct approach to identifying security vulnerabilities or outdated security updates. It doesn’t provide specific information about the security status of the virtual machines in question.
You are currently managing workloads running on Windows Server for which your company owns the licenses. Your workloads are only needed during working hours, which allows you to shut down the instances during the weekend. Your Windows Server licenses are up for renewal in a month, and you want to optimize your license cost.
What should you do?
A. Renew your licenses for an additional period of 3 years. Renew your licenses for an additional period of 3 years. Negotiate a cost reduction with your current hosting provider wherein infrastructure cost is reduced when workloads are not in use
B. Renew your licenses for an additional period of 2 years. Negotiate a cost reduction by committing to an automatic renewal of the licenses at the end of the 2 year period
C. Migrate the workloads to Compute Engine with a bring-your-own-license (BYOL) model
D. Migrate the workloads to Compute Engine with a pay-as-you-go (PAYG) model
To optimize license costs for workloads running on Windows Server, given the requirement of usage only during working hours and the need for license renewal, the most suitable option is:
C. Migrate the workloads to Compute Engine with a bring-your-own-license (BYOL) model.
Explanation:
- Optimizing License Costs: The BYOL model allows you to use your existing Windows Server licenses on Compute Engine, which can help optimize costs. Since you already own the licenses, utilizing them on the cloud platform can reduce additional licensing expenses.
- Flexibility in Usage: Migrating to Compute Engine with a BYOL model allows you to shut down instances during the weekends or any other periods when the workloads are not needed, effectively minimizing usage and cost during non-working hours.
Options A and B involve renewing licenses for additional periods, but considering the requirement for flexibility and cost optimization based on usage, it’s prudent to explore alternatives that offer cost-efficiency and better alignment with workload needs.
Option D (PAYG model) might result in unnecessary costs during weekends when the workloads are not needed, which is not in line with the goal of optimizing license costs.
In summary, migrating to Compute Engine with a BYOL model allows you to use your existing licenses, optimize costs, and tailor usage to fit your workload’s specific requirements.
Your organization runs a distributed application in the Compute Engine virtual machines. Your organization needs redundancy, but it also needs extremely fast communication (less than 10 milliseconds) between the parts of the application in different virtual machines.
Where should your organization locate this virtual machines?
A. In a single zone within a single region
B. In different zones within a single region
C. In multiple regions, using one zone per region
D. In multiple regions, using multiple zones per region
For achieving redundancy and extremely fast communication (less than 10 milliseconds) between parts of a distributed application in different virtual machines, the most suitable option would be:
B. In different zones within a single region.
Explanation:
- Redundancy: Placing the virtual machines in different zones within a single region provides redundancy. If one zone experiences an issue or failure, the application can continue running in another zone within the same region, ensuring high availability and reliability.
- Fast Communication: Keeping the virtual machines in different zones within a single region allows for fast communication (less than 10 milliseconds) between parts of the application. Zones within a region are geographically close, minimizing latency and ensuring speedy communication.
Option A (in a single zone within a single region) doesn’t provide the desired level of redundancy, as a failure in that zone could lead to downtime.
Option C (multiple regions, using one zone per region) and Option D (multiple regions, using multiple zones per region) might introduce higher latency due to the geographical distance between regions or potential inter-region communication delays, which could exceed the specified requirement of less than 10 milliseconds for communication.
Therefore, for a balance of redundancy and fast communication, placing the virtual machines in different zones within a single region is the optimal choice.
An organization decides to migrate their on-premises environment to the cloud. They need to determine which resource components still need to be assigned ownership.
Which two functions does a public cloud provider own? (Choose two.)
A. Hardware maintenance
B. Infrastructure architecture
C. Infrastructure deployment automation
D. Hardware capacity management
E. Fixing application security issues
In a typical public cloud service model, the responsibilities between the cloud provider and the cloud customer are divided based on the service type. For Infrastructure as a Service (IaaS), the cloud provider typically owns functions related to hardware maintenance and hardware capacity management. For Platform as a Service (PaaS) and Software as a Service (SaaS), the provider takes care of additional layers of the stack, including infrastructure architecture and automation, as well as application security issues. Therefore, the two functions a public cloud provider generally owns are:
A. Hardware maintenance
- Public cloud providers are responsible for maintaining and managing the physical hardware, including servers, storage, networking, etc., to ensure reliability and performance of the cloud infrastructure.
D. Hardware capacity management
- The cloud provider manages and optimizes hardware capacity to ensure that resources are available to meet the needs of various cloud customers without any performance degradation.
While it’s important to note that the specific responsibilities can vary based on the cloud provider and the service model being used (IaaS, PaaS, SaaS), these functions are generally owned by the public cloud provider in a traditional cloud service model.
You are a program manager within a Software as a Service (SaaS) company that offers rendering software for animation studios. Your team needs the ability to allow scenes to be scheduled at will and to be interrupted at any time to restart later. Any individual scene rendering takes less than 12 hours to complete, and there is no service-level agreement (SLA) for the completion time for all scenes. Results will be stored in a global Cloud Storage bucket. The compute resources are not bound to any single geographical location. This software needs to run on Google Cloud in a cost-optimized way.
What should you do?
A. Deploy the application on Compute Engine using preemptible instances
B. Develop the application so it can run in an unmanaged instance group
C. Create a reservation for the minimum number of Compute Engine instances you will use
D. Start more instances with fewer virtual centralized processing units (vCPUs) instead of fewer instances with more vCPUs
For a cost-optimized and efficient approach to running the rendering software in a Software as a Service (SaaS) environment on Google Cloud, the most suitable option would be:
A. Deploy the application on Compute Engine using preemptible instances.
Explanation:
- Preemptible Instances: Preemptible instances are cost-effective and suitable for short-lived, interruptible workloads like rendering scenes. Since individual scene rendering takes less than 12 hours and can be interrupted and restarted later, preemptible instances are a good fit. They are considerably cheaper than regular instances but can be terminated by the system at any time with a 30-second notice.
- Cost Optimization: Preemptible instances are cost-effective due to their lower price, making them ideal for rendering workloads. Even if an instance is terminated, you can set up the software to handle interruptions gracefully and restart the rendering process.
Option B (unmanaged instance group) might not be the best fit, as preemptible instances offer more cost savings and flexibility in this scenario.
Option C (creating a reservation for a minimum number of instances) may not align well with the variable workload demands and the need for cost optimization.
Option D (starting more instances with fewer vCPUs) may not be the most cost-effective approach as it’s generally better to use preemptible instances for this type of workload, which can provide the needed resources at a lower cost.
In summary, using preemptible instances on Compute Engine is a cost-effective and efficient solution for running rendering workloads with the ability to schedule and restart scenes while storing results in a global Cloud Storage bucket.
Your manager wants to restrict communication of all virtual machines with internet access; with resources in another network; or with a resource outside Compute
Engine. It is expected that different teams will create new folders and projects in the near future.
How would you restrict all virtual machines from having an external IP address?
A. Define an organization policy at the root organization node to restrict virtual machine instances from having an external IP address
B. Define an organization policy on all existing folders to define a constraint to restrict virtual machine instances from having an external IP address
C. Define an organization policy on all existing projects to restrict virtual machine instances from having an external IP address
D. Communicate with the different teams and agree that each time a virtual machine is created, it must be configured without an external IP address
To restrict all virtual machines from having an external IP address in a way that accommodates future projects and teams, the most appropriate option would be:
A. Define an organization policy at the root organization node to restrict virtual machine instances from having an external IP address.
Explanation:
- Organization Policy at the Root Level: Defining the policy at the root organization node ensures that the restriction is enforced across the entire organization, including current and future projects and folders. This approach ensures consistency and adherence to the policy organization-wide.
- Future Projects and Teams: By setting the policy at the root organization node, you ensure that any new folders or projects created in the future will inherit this policy, simplifying management and ensuring compliance without needing explicit communication with every team.
Option B (defining an organization policy on all existing folders) and Option C (defining an organization policy on all existing projects) would require applying the policy individually to each folder or project, making it less scalable and more prone to oversight as new folders or projects are added.
Option D (communicating with different teams to configure virtual machines without an external IP address each time) is not a scalable solution and can lead to inconsistent implementation and potential security risks if overlooked by teams.
In summary, defining an organization policy at the root organization node is the most effective way to ensure consistent enforcement of restricting virtual machine instances from having an external IP address across the organization, including current and future projects and teams.
Your multinational organization has servers running mission-critical workloads on its premises around the world. You want to be able to manage these workloads consistently and centrally, and you want to stop managing infrastructure.
What should your organization do?
A. Migrate the workloads to a public cloud
B. Migrate the workloads to a central office building
C. Migrate the workloads to multiple local co-location facilities
D. Migrate the workloads to multiple local private clouds
To centralize workload management, eliminate the need to manage infrastructure, and achieve consistent management across a multinational organization, the most suitable option is:
A. Migrate the workloads to a public cloud.
Explanation:
- Centralized Management: Public clouds provide centralized management tools and platforms that allow you to manage workloads consistently from a central location. These platforms offer centralized monitoring, scaling, security, and more, allowing for efficient management without the need to manage physical infrastructure across diverse locations.
- Eliminate Infrastructure Management: By migrating to a public cloud, the organization can offload the responsibility of managing the underlying infrastructure, including hardware, networking, and storage, to the cloud service provider. This allows the organization to focus on managing the workloads and applications, reducing the burden of infrastructure management.
- Global Reach: Public clouds have a global presence with data centers located around the world. This enables the organization to place workloads close to their end-users, optimizing performance and reducing latency.
Options B, C, and D involve managing infrastructure in various ways, which goes against the goal of stopping infrastructure management. Option A (migrating to a public cloud) aligns with the organization’s objective of centralizing management and eliminating the need to manage physical infrastructure while providing a global reach for workloads.
Your organization stores highly sensitive data on-premises that cannot be sent over the public internet. The data must be processed both on-premises and in the cloud.
What should your organization do?
A. Configure Identity-Aware Proxy (IAP) in your Google Cloud VPC network
B. Create a Cloud VPN tunnel between Google Cloud and your data center
C. Order a Partner Interconnect connection with your network provider
D. Enable Private Google Access in your Google Cloud VPC network
Given the requirement to process highly sensitive data both on-premises and in the cloud without sending data over the public internet, the most appropriate option is:
B. Create a Cloud VPN tunnel between Google Cloud and your data center.
Explanation:
- Secure Communication: Cloud VPN provides a secure and encrypted tunnel between your on-premises data center and Google Cloud. This ensures that the sensitive data is transmitted securely without exposure to the public internet.
- Hybrid Cloud Processing: Cloud VPN allows for seamless and secure communication between on-premises resources and Google Cloud, enabling the processing of data both on-premises and in the cloud as needed.
Option A (Identity-Aware Proxy) is used for securing applications running in the cloud and may not be directly related to establishing a secure connection for data transfer between on-premises and cloud environments.
Option C (Partner Interconnect) involves a direct physical connection between your network and Google’s network, which may not be necessary for the given requirement and could be more complex than needed.
Option D (Private Google Access) allows VM instances in a VPC network to reach Google services without a public IP address, but it doesn’t directly address the need for secure communication between on-premises and cloud environments.
In summary, creating a secure Cloud VPN tunnel between your data center and Google Cloud ensures the data can be processed securely both on-premises and in the cloud without using the public internet.
Your company’s development team is building an application that will be deployed on Cloud Run. You are designing a CI/CD pipeline so that any new version of the application can be deployed in the fewest number of steps possible using the CI/CD pipeline you are designing. You need to select a storage location for the images of the application after the CI part of your pipeline has built them.
What should you do?
A. Create a Compute Engine image containing the application
B. Store the images in Container Registry
C. Store the images in Cloud Storage
D. Create a Compute Engine disk containing the application
For storing images of the application in the CI/CD pipeline for efficient deployment on Cloud Run, the most appropriate option is:
B. Store the images in Container Registry.
Explanation:
- Container Registry: Container Registry is designed specifically for storing container images, making it a suitable choice for storing application images in a containerized environment like Cloud Run.
- Efficient Deployment: Cloud Run is designed to deploy containerized applications. By storing the application images in Container Registry, you streamline the deployment process, making it easy to deploy new versions of the application to Cloud Run.
Option A (Compute Engine image) and Option D (Compute Engine disk) are not appropriate for deploying applications on Cloud Run, which is a serverless container-based service.
Option C (Cloud Storage) is a viable option for storing various types of files, including container images, but Container Registry is specifically tailored for storing and managing container images, making it the more appropriate choice for containerized applications intended for deployment on Cloud Run.
In summary, storing the application images in Container Registry ensures an efficient deployment process for Cloud Run, enabling quick and streamlined deployment of new versions of the application.
Each of the three cloud service models - infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) - offers benefits between flexibility and levels of management by the cloud provider and the customer.
Why would SaaS be the right choice of service model?
A. You want a balance between flexibility for the customer and the level of management by the cloud provider
B. You want to minimize the level of management by the customer
C. You want to maximize flexibility for the customer.
D. You want to be able to shift your emphasis between flexibility and management by the cloud provider as business needs change
The correct choice of service model depends on the specific requirements and preferences of an organization. Given the options provided, if the objective is to minimize the level of management by the customer while still benefitting from the service, the most appropriate option would be:
B. You want to minimize the level of management by the customer.
Explanation:
- SaaS Minimizes Management: In a SaaS model, the cloud provider manages almost everything, including infrastructure, software, updates, security, and maintenance. Customers simply use the software through a web browser, without the need to manage underlying technical complexities.
- Ease of Use: SaaS provides an easy-to-use solution where customers can access and use the software without worrying about the backend infrastructure, making it highly convenient and reducing the management burden on the customer.
While options A, C, and D may align with other objectives and use cases, if the primary goal is to minimize the level of management and focus on using the software without getting involved in its technical aspects, then SaaS is the right choice.
As your organization increases its release velocity, the VM-based application upgrades take a long time to perform rolling updates due to OS boot times. You need to make the application deployments faster.
What should your organization do?
A. Migrate your VMs to the cloud, and add more resources to them
B. Convert your applications into containers
C. Increase the resources of your VMs
D. Automate your upgrade rollouts
To accelerate application deployments and improve release velocity by minimizing OS boot times and simplifying the deployment process, the most effective approach would be:
B. Convert your applications into containers.
Explanation:
- Containerization: Containers provide a lightweight, portable, and consistent environment for applications. They encapsulate the application, its dependencies, and configurations, making it easy to run consistently across various environments without worrying about differences in underlying systems or boot times.
- Faster Deployments: Containers can be started, stopped, and scaled very quickly since they share the host OS kernel. This significantly reduces deployment times compared to traditional VM-based deployments, where OS boot times can be a bottleneck.
- Portability and Consistency: Containers can be run on any system that supports the container runtime, ensuring consistent behavior and reducing the risk of deployment-related issues.
Option A (adding more resources to VMs) and Option C (increasing the resources of VMs) may alleviate some performance issues but won’t address the fundamental problem of long OS boot times and the agility required for faster deployments.
Option D (automating upgrade rollouts) is important and should be part of the solution, but it may not address the root issue of long OS boot times that significantly impact deployment speed.
In summary, converting applications into containers (Option B) is the most effective way to improve application deployment speed and release velocity by minimizing OS boot times and enabling faster, more efficient deployments. Additionally, automating upgrade rollouts (Option D) can further enhance deployment efficiency and consistency.
Your organization uses Active Directory to authenticate users. Users’ Google account access must be removed when their Active Directory account is terminated.
How should your organization meet this requirement?
A. Configure two-factor authentication in the Google domain
B. Remove the Google account from all IAM policies
C. Configure BeyondCorp and Identity-Aware Proxy in the Google domain
D. Configure single sign-on in the Google domain
To ensure that users’ Google account access is removed when their Active Directory account is terminated, the most appropriate option is:
D. Configure single sign-on in the Google domain.
Explanation:
- Single Sign-On (SSO): SSO allows users to sign in to multiple applications using a single set of credentials. When configured with Active Directory, it ensures that access to Google accounts is tied to the Active Directory account. When an Active Directory account is terminated, access to associated Google accounts can be automatically revoked.
- Integration with Active Directory: By integrating SSO with Active Directory, the termination of an Active Directory account will effectively disable the user’s access to the Google domain, ensuring compliance with the requirement.
Option A (configuring two-factor authentication) is a security measure but does not directly address the requirement to remove Google account access when an Active Directory account is terminated.
Option B (removing the Google account from all IAM policies) is related to Google Cloud IAM (Identity and Access Management) and may not be directly tied to Active Directory account termination.
Option C (configuring BeyondCorp and Identity-Aware Proxy) is a security model but does not specifically address the synchronization of account terminations between Active Directory and Google accounts.
In summary, configuring single sign-on (SSO) in the Google domain, integrating it with Active Directory, is the most appropriate approach to ensure that Google account access is removed when the corresponding Active Directory account is terminated.
Your company has recently acquired three growing startups in three different countries. You want to reduce overhead in infrastructure management and keep your costs low without sacrificing security and quality of service to your customers.
How should you meet these requirements?
A. Host all your subsidiaries’ services on-premises together with your existing services.
B. Host all your subsidiaries’ services together with your existing services on the public cloud.
C. Build a homogenous infrastructure at each subsidiary, and invest in training their engineers.
D. Build a homogenous infrastructure at each subsidiary, and invest in hiring more engineers.
To reduce overhead in infrastructure management, keep costs low, maintain security, and ensure the quality of service for customers across recently acquired startups in different countries, the most effective approach would be:
B. Host all your subsidiaries’ services together with your existing services on the public cloud.
Explanation:
- Public Cloud Benefits: Leveraging the public cloud allows for reduced infrastructure management overhead as the cloud provider handles the underlying infrastructure, including maintenance, updates, and security. It also offers scalability and flexibility based on demand, helping to control costs and adapt to growth efficiently.
- Consolidation and Integration: By hosting all services, including those of the acquired subsidiaries, on a unified public cloud platform, you can consolidate resources, reduce complexity, and improve integration across different parts of the organization.
- Cost Efficiency: Public cloud providers often offer cost-effective solutions with pay-as-you-go models, allowing you to manage costs effectively. Additionally, shared resources and centralized management lead to cost savings compared to separate on-premises or localized infrastructures.
Options A, C, and D involve building or maintaining separate infrastructures at each subsidiary, which can lead to increased complexity, higher costs, and challenges in maintaining consistency, security, and quality of service.
In summary, hosting all subsidiaries’ services, along with existing services, on the public cloud offers a scalable, cost-effective, and streamlined approach to infrastructure management while ensuring security and quality of service.
What is the difference between Standard and Coldline storage?
A. Coldline storage is for data for which a slow transfer rate is acceptable.
B. Standard and Coldline storage have different durability guarantees.
C. Standard and Coldline storage use different APIs.
D. Coldline storage is for infrequently accessed data.
The difference between Standard and Coldline storage in Google Cloud is best described by:
D. Coldline storage is for infrequently accessed data.
Explanation:
-
Standard Storage:
- Standard storage is designed for data that is accessed frequently or in real-time.
- It offers a higher storage cost but provides faster access to data.
- Ideal for data that requires high availability and low-latency access.
-
Coldline Storage:
- Coldline storage is intended for data that is accessed infrequently or accessed very rarely.
- It offers a lower storage cost compared to Standard storage, but access and retrieval may take longer.
- Designed for data that is stored for a longer duration and accessed less frequently.
Options A, B, and C are not accurate explanations for the difference between Standard and Coldline storage:
- Option A (slow transfer rate): Coldline storage is not about transfer rate; it’s about infrequent access to data.
- Option B (durability guarantees): Both Standard and Coldline storage have the same durability guarantees, meaning data is extremely durable in both storage classes.
- Option C (different APIs): Both storage classes use the same APIs for access and management; the difference is in usage and pricing based on the storage class selected.
What would provide near-unlimited availability of computing resources without requiring your organization to procure and provision new equipment?
A. Public cloud
B. Containers
C. Private cloud
D. Microservices
To provide near-unlimited availability of computing resources without requiring your organization to procure and provision new equipment, the most appropriate option is:
A. Public cloud.
Explanation:
-
Public Cloud:
- Public cloud services offer vast and scalable computing resources provided by cloud service providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others.
- Public cloud environments allow organizations to access computing resources on-demand without the need to purchase and manage physical infrastructure.
- Scalability is a key feature, enabling organizations to easily scale up or down based on demand, ensuring near-unlimited availability of computing resources.
-
Containers, Private Cloud, and Microservices:
- While containers, private cloud, and microservices can provide scalability and flexibility, they may still be limited by the physical infrastructure of an organization or have specific scalability constraints compared to the virtually unlimited resources available in public clouds.
In summary, public cloud platforms offer the ability to access near-unlimited computing resources without the need for organizations to procure and provision new physical equipment, making it the most suitable option for achieving high availability and scalability.
You are a program manager for a team of developers who are building an event-driven application to allow users to follow one another’s activities in the app. Each time a user adds himself as a follower of another user, a write occurs in the real-time database.
The developers will develop a lightweight piece of code that can respond to database writes and generate a notification to let the appropriate users know that they have gained new followers. The code should integrate with other cloud services such as Pub/Sub, Firebase, and Cloud APIs to streamline the orchestration process. The application requires a platform that automatically manages underlying infrastructure and scales to zero when there is no activity.
Which primary compute resource should your developers select, given these requirements?
A. Google Kubernetes Engine
B. Cloud Functions
C. App Engine flexible environment
D. Compute Engine
Given the requirements of building an event-driven application that automatically manages underlying infrastructure, scales to zero during periods of inactivity, and integrates with various cloud services for orchestration, the most suitable primary compute resource would be:
B. Cloud Functions
Explanation:
- Event-Driven Architecture: Cloud Functions are designed for event-driven, serverless computing. They respond to events, such as database writes, making them ideal for triggering actions like generating notifications whenever a user gains new followers.
- Automated Infrastructure Management: Cloud Functions abstract away infrastructure management, automatically scaling up or down based on the number of events and activity, thus meeting the requirement of automatically managing the underlying infrastructure.
- Integration with Cloud Services: Cloud Functions can seamlessly integrate with other cloud services such as Pub/Sub, Firebase, and Cloud APIs, allowing for streamlined orchestration of processes and interactions with different parts of the application.
- Cost-Efficiency: Cloud Functions follow a pay-as-you-go model, incurring costs only when they’re triggered by events. When there’s no activity, they scale down to zero, ensuring cost-efficiency during periods of inactivity.
Google Kubernetes Engine (A) and App Engine flexible environment (C) are also good choices, but Cloud Functions align more closely with the requirements of a lightweight, event-driven, and serverless architecture, providing cost-efficiency through automatic scaling down to zero during inactivity.
Compute Engine (D) is not the optimal choice for this scenario as it involves manual infrastructure management and does not align well with the requirement of automatically managing and scaling based on events and activity.
Your organization is developing an application that will capture a large amount of data from millions of different sensor devices spread all around the world. Your organization needs a database that is suitable for worldwide, high-speed data storage of a large amount of unstructured data.
Which Google Cloud product should your organization choose?
A. Firestore
B. Cloud Data Fusion
C. Cloud SQL
D. Cloud Bigtable
For capturing a large amount of unstructured data from millions of sensor devices spread worldwide and needing high-speed data storage, the most suitable Google Cloud product is:
D. Cloud Bigtable
Explanation:
- Scalability and High-Speed Data Storage: Cloud Bigtable is designed for handling large-scale, high-throughput workloads with a focus on performance and scalability. It is a NoSQL, massively scalable, and highly available database service that can handle massive amounts of unstructured data.
- Global Deployment: Cloud Bigtable supports worldwide deployment, enabling efficient data ingestion from millions of sensor devices spread across the globe. It can manage the high-speed, high-volume writes and reads necessary for such a use case.
Firestore (A) is a NoSQL document database that offers scalability and real-time synchronization but may not be as suitable for extremely high-speed and high-volume unstructured data storage compared to Cloud Bigtable.
Cloud Data Fusion (B) is a fully managed, cloud-native data integration service but is more focused on data integration and transformation rather than high-speed unstructured data storage.
Cloud SQL (C) is a fully managed relational database service, which is not ideal for unstructured data storage and may not have the scalability and performance needed for this use case.
In summary, Cloud Bigtable is the appropriate Google Cloud product for worldwide, high-speed data storage of a large amount of unstructured data from millions of sensor devices.
Your organization needs to build streaming data pipelines. You don’t want to manage the individual servers that do the data processing in the pipelines. Instead, you want a managed service that will automatically scale with the amount of data to be processed.
Which Google Cloud product or feature should your organization choose?
A. Pub/Sub
B. Dataflow
C. Data Catalog
D. Dataprep by Trifacta
For building streaming data pipelines without managing individual servers and ensuring automatic scaling with the data volume, the most suitable Google Cloud product is:
B. Dataflow
Explanation:
- Managed Service and Automatic Scaling: Google Cloud Dataflow is a fully managed service that allows you to design, deploy, and monitor data processing pipelines. It automatically handles server provisioning, scaling, and managing the infrastructure based on the incoming data volume, ensuring you don’t have to manage individual servers.
- Stream Processing: Dataflow supports stream processing, making it an ideal choice for building streaming data pipelines. It can handle real-time data processing with scalability based on the incoming data stream.
Pub/Sub (A) is a messaging service and can be used in conjunction with Dataflow for ingesting and delivering messages to the data processing pipeline.
Data Catalog (C) is a fully managed and scalable metadata management service, primarily used for discovering and managing metadata across an organization. It is not specifically designed for building and managing streaming data pipelines.
Dataprep by Trifacta (D) is a cloud-based service for cleaning, enriching, and transforming raw data into a usable format. While it’s useful for data preparation, it’s not focused on building and managing streaming data pipelines.
In summary, Google Cloud Dataflow is the appropriate choice for building streaming data pipelines without managing individual servers, ensuring automatic scaling based on the volume of incoming data.
Your organization is building an application running in Google Cloud. Currently, software builds, tests, and regular deployments are done manually, but you want to reduce work for the team. Your organization wants to use Google Cloud managed solutions to automate your build, testing, and deployment process.
Which Google Cloud product or feature should your organization use?
A. Cloud Scheduler
B. Cloud Code
C. Cloud Build
D. Cloud Deployment Manager
To automate the build, testing, and deployment process in Google Cloud, the most appropriate Google Cloud product is:
C. Cloud Build
Explanation:
- Automated Build and Test: Cloud Build is a fully managed continuous integration and continuous deployment (CI/CD) platform that automates the build and test processes. It allows you to automatically build, test, and validate code changes upon every commit or triggered event.
- Integration with Other Services: Cloud Build integrates with other Google Cloud services and tools, making it easy to set up pipelines that automate your development workflows.
Cloud Scheduler (A) is a fully managed cron job scheduler, which is useful for invoking services at specified intervals, but it doesn’t directly handle the build, test, and deployment automation process.
Cloud Code (B) is an extension for IDEs like Visual Studio Code and IntelliJ IDEA that helps with writing, deploying, and debugging cloud-native applications. While it assists in the development process, it’s not a standalone automation solution for build, test, and deployment.
Cloud Deployment Manager (D) is a tool to define, deploy, and manage infrastructure in Google Cloud, helping in creating and managing cloud resources in a declarative manner. While it’s essential for infrastructure deployment, it’s not focused on automating the entire build, test, and deployment process.
In summary, Cloud Build is the appropriate Google Cloud product for automating the build, testing, and deployment process, streamlining the development workflow, and reducing manual work for the team.
Which Google Cloud product can report on and maintain compliance on your entire Google Cloud organization to cover multiple projects?
A. Cloud Logging
B. Identity and Access Management
C. Google Cloud Armor
D. Security Command Center
The Google Cloud product that can report on and maintain compliance for your entire Google Cloud organization covering multiple projects is:
D. Security Command Center
Explanation:
- Security Command Center: Google Cloud Security Command Center (SCC) is a security and risk management platform that helps you gain centralized visibility into your security posture across your Google Cloud environment. It provides security and compliance insights and enables monitoring, detection, and response to security threats and vulnerabilities.
Cloud Logging (A) is a tool for storing, searching, analyzing, and alerting on log data. While it’s essential for monitoring and analyzing logs, it’s not primarily focused on reporting and maintaining compliance across the entire organization.
Identity and Access Management (B) is a critical component for controlling access and permissions within Google Cloud, but it’s more focused on access control than reporting and maintaining compliance at an organizational level.
Google Cloud Armor (C) is a DDoS (Distributed Denial of Service) and application defense service, providing security for web applications and services. It’s not specifically designed for reporting and maintaining compliance across multiple projects at an organizational level.
In summary, Security Command Center (D) is the Google Cloud product that provides centralized visibility and management of security and compliance across the entire Google Cloud organization, covering multiple projects.
Your organization needs to establish private network connectivity between its on-premises network and its workloads running in Google Cloud. You need to be able to set up the connection as soon as possible.
Which Google Cloud product or feature should you use?
A. Cloud Interconnect
B. Direct Peering
C. Cloud VPN
D. Cloud CDN
To establish private network connectivity between your on-premises network and workloads running in Google Cloud quickly, the most appropriate Google Cloud product or feature is:
C. Cloud VPN (Virtual Private Network)
Explanation:
- Private Network Connectivity: Cloud VPN provides a secure and encrypted connection between your on-premises network and your virtual private cloud (VPC) network in Google Cloud. It allows you to securely connect your on-premises network to your Google Cloud workloads.
- Quick Setup: Cloud VPN is relatively easy and quick to set up, allowing you to establish the connection promptly.
Cloud Interconnect (A) and Direct Peering (B) are both valid options for private network connectivity but may involve longer setup times and additional configurations compared to Cloud VPN, making Cloud VPN more suitable when the requirement is to set up the connection quickly.
Cloud CDN (D) is a content delivery network service and is not related to setting up a private network connection between on-premises and Google Cloud.
In summary, to establish private network connectivity quickly between your on-premises network and workloads in Google Cloud, Cloud VPN is the most appropriate choice.
Your organization is developing a mobile app and wants to select a fully featured cloud-based compute platform for it.
Which Google Cloud product or feature should your organization use?
A. Google Kubernetes Engine
B. Firebase
C. Cloud Functions
D. App Engine
For developing a mobile app and selecting a fully featured cloud-based compute platform, the most appropriate Google Cloud product is:
B. Firebase
Explanation:
- Firebase: Firebase is a comprehensive mobile and web application development platform provided by Google. It offers a wide range of features including real-time database, authentication, hosting, analytics, machine learning, and more, making it ideal for developing and managing mobile apps.
- Mobile App Development: Firebase is specifically designed to support mobile app development, offering features that facilitate authentication, real-time database updates, cloud messaging, and other functionalities critical for mobile apps.
Google Kubernetes Engine (A) is a managed Kubernetes service and is better suited for deploying, managing, and orchestrating containerized applications. While it can be used to support a mobile app backend, it may be more complex than necessary for a mobile app development scenario.
Cloud Functions (C) is a serverless compute service that allows developers to run event-driven functions in response to events. While useful for backend logic and processing, it may not cover the broader set of features required for a fully featured cloud-based compute platform for mobile app development.
App Engine (D) is a platform-as-a-service (PaaS) offering that enables the deployment and scaling of applications. It is well-suited for web applications and backends, but Firebase is more specialized and comprehensive for mobile app development needs.
In summary, Firebase is the most suitable Google Cloud product for a fully featured cloud-based compute platform specifically designed for mobile app development, providing a wide array of features critical for mobile apps.
Your company has been using a shared facility for data storage and will be migrating to Google Cloud. One of the internal applications uses Linux custom images that need to be migrated.
Which Google Cloud product should you use to maintain the custom images?
A. App Engine flexible environment
B. Compute Engine
C. App Engine standard environment
D. Google Kubernetes Engine
To maintain the custom Linux images during the migration to Google Cloud, the most appropriate Google Cloud product is:
B. Compute Engine
Explanation:
- Compute Engine: Compute Engine allows you to create and manage custom Linux images easily. You can create, customize, and store your custom Linux images, including any specific configurations or software setups needed for your internal application. These custom images can then be used to create and manage virtual machines (VMs) in Google Cloud.
App Engine flexible environment (A) and App Engine standard environment (C) are platform-as-a-service (PaaS) offerings designed for deploying applications without having direct control over the underlying infrastructure. These environments do not provide direct support for managing custom Linux images like Compute Engine does.
Google Kubernetes Engine (D) is a managed Kubernetes service and is more focused on orchestrating and managing containerized applications using Kubernetes. It is not designed for managing custom Linux images in the same way Compute Engine is.
In summary, Compute Engine is the most suitable Google Cloud product for maintaining custom Linux images during the migration, providing flexibility and control over the custom images needed for your internal application.
Your organization wants to migrate its data management solutions to Google Cloud because it needs to dynamically scale up or down and to run transactional
SQL queries against historical data at scale. Which Google Cloud product or service should your organization use?
A. BigQuery
B. Cloud Bigtable
C. Pub/Sub
D. Cloud Spanner
To dynamically scale up or down and run transactional SQL queries against historical data at scale, the most appropriate Google Cloud product or service is:
D. Cloud Spanner
Explanation:
- Cloud Spanner: Cloud Spanner is a globally distributed, horizontally scalable, strongly consistent, and relational database service. It provides the ability to scale up or down dynamically based on workload demands. It allows you to run transactional SQL queries against historical data at scale, ensuring consistent, ACID-compliant transactions.
- Transactional SQL Queries: Cloud Spanner supports SQL-based queries, making it suitable for transactional workloads where you need to perform SQL queries against historical data while maintaining strong consistency.
BigQuery (A) is an excellent choice for running analytical queries on large datasets, but it may not be the best fit for transactional SQL queries or for dynamic scaling up or down based on workload demands.
Cloud Bigtable (B) is a high-throughput, scalable NoSQL database, but it is more suitable for handling high-velocity, high-volume analytical workloads rather than transactional SQL queries.
Pub/Sub (C) is a messaging service for building event-driven systems and real-time analytics, but it’s not a database solution that allows transactional SQL queries against historical data.
In summary, for dynamically scaling and running transactional SQL queries against historical data at scale, Cloud Spanner is the most appropriate Google Cloud product.
Your organization needs to categorize objects in a large group of static images using machine learning. Which Google Cloud product or service should your organization use?
A. BigQuery ML
B. AutoML Video Intelligence
C. Cloud Vision API
D. AutoML Tables
To categorize objects in a large group of static images using machine learning, the most appropriate Google Cloud product or service is:
C. Cloud Vision API
Explanation:
- Cloud Vision API: Cloud Vision API is a powerful and efficient image analysis tool that can be used to categorize and annotate images. It can detect and identify objects, faces, logos, labels, and more within images, making it ideal for categorizing objects in a large group of static images.
BigQuery ML (A) is a machine learning service that is more suitable for working with structured data and performing machine learning tasks directly within BigQuery using SQL. It is not specifically designed for image categorization.
AutoML Video Intelligence (B) is designed for training machine learning models specifically for videos, not static images. It’s not the most suitable choice for this scenario.
AutoML Tables (D) is used for structured tabular data and is not designed for image categorization tasks.
In summary, Cloud Vision API is the most appropriate Google Cloud product for categorizing objects in a large group of static images using machine learning.
Your organization runs all its workloads on Compute Engine virtual machine instances. Your organization has a security requirement: the virtual machines are not allowed to access the public internet. The workloads running on those virtual machines need to access BigQuery and Cloud Storage, using their publicly accessible interfaces, without violating the security requirement.
Which Google Cloud product or feature should your organization use?
A. Identity-Aware Proxy
B. Cloud NAT (network address translation)
C. VPC internal load balancers
D. Private Google Access
To enable the workloads running on Compute Engine virtual machine instances to access BigQuery and Cloud Storage using their publicly accessible interfaces without allowing access to the public internet, the most appropriate Google Cloud product or feature is:
D. Private Google Access
Explanation:
- Private Google Access: Private Google Access allows virtual machine instances without public IP addresses to reach Google APIs and services such as BigQuery and Cloud Storage using their publicly accessible interfaces. It enables the workloads to access these services without violating the security requirement of not allowing access to the public internet.
- Identity-Aware Proxy (A): Identity-Aware Proxy is used to control access to applications and VMs, providing secure access to your applications without exposing them to the public internet. However, it’s not directly related to enabling access to public Google services without public IP addresses.
- Cloud NAT (B): Cloud NAT is used to provide internet connectivity to instances that do not have a public IP address. However, in this scenario, the goal is to access Google services without exposing the VMs to the public internet.
- VPC Internal Load Balancers (C): VPC Internal Load Balancers are used to load balance traffic within a VPC. They do not specifically address the requirement of allowing access to public Google services without public IP addresses.
In summary, to meet the security requirement and enable access to BigQuery and Cloud Storage through their publicly accessible interfaces without allowing access to the public internet, Private Google Access (option D) is the most appropriate Google Cloud product or feature.
Which Google Cloud product is designed to reduce the risks of handling personally identifiable information (PII)?
A. Cloud Storage
B. Google Cloud Armor
C. Cloud Data Loss Prevention
D. Secret Manager
The Google Cloud product designed to reduce the risks of handling personally identifiable information (PII) is:
C. Cloud Data Loss Prevention
Explanation:
- Cloud Data Loss Prevention (DLP): Cloud DLP is a comprehensive service that helps you discover, classify, and protect sensitive data, including personally identifiable information (PII). It provides tools to automatically scan and identify PII and other sensitive information, allowing you to apply appropriate controls and protections to mitigate the risks associated with handling such data.
- Cloud Storage (A): Cloud Storage is a scalable object storage solution, but it does not specifically focus on data loss prevention or protection of PII.
- Google Cloud Armor (B): Google Cloud Armor is a DDoS and application defense service, focused on protecting against application vulnerabilities and DDoS attacks. It’s not specifically designed to handle or protect PII.
- Secret Manager (D): Secret Manager is designed for securely storing API keys, passwords, certificates, and other sensitive data. While it enhances security, it’s not primarily focused on PII protection.
In summary, Cloud Data Loss Prevention (Cloud DLP) is the most appropriate Google Cloud product for reducing the risks associated with handling personally identifiable information (PII) and ensuring proper protection and management of sensitive data.
Your organization is migrating to Google Cloud. As part of that effort, it needs to move terabytes of data from on-premises file servers to Cloud Storage. Your organization wants the migration process to be automated and to be managed by Google. Your organization has an existing Dedicated Interconnect connection that it wants to use. Which Google Cloud product or feature should your organization use?
A. Storage Transfer Service
B. Migrate for Anthos
C. BigQuery Data Transfer Service
D. Transfer Appliance
To automate and manage the migration of terabytes of data from on-premises file servers to Google Cloud Storage using an existing Dedicated Interconnect connection, the most appropriate Google Cloud product or feature is:
A. Storage Transfer Service
Explanation:
- Storage Transfer Service: Storage Transfer Service allows you to transfer large amounts of data from on-premises file servers or other cloud providers to Google Cloud Storage. It supports scheduling and automating these transfers, making it a suitable choice for automating the migration process. The service can leverage an existing Dedicated Interconnect connection to facilitate the migration securely and efficiently.
- Migrate for Anthos (B): Migrate for Anthos is a service designed to migrate virtual machines and their workloads to Google Kubernetes Engine (GKE). It’s not specifically designed for migrating large amounts of data from file servers to Google Cloud Storage.
- BigQuery Data Transfer Service (C): BigQuery Data Transfer Service is designed to transfer data into BigQuery for analysis. It’s not appropriate for moving terabytes of data from on-premises file servers to Cloud Storage.
- Transfer Appliance (D): Transfer Appliance is a physical hardware device that allows you to securely and quickly move large amounts of data to Google Cloud Storage. However, it’s not necessary to use this physical appliance when you have an existing Dedicated Interconnect connection, as Storage Transfer Service can accomplish the transfer over the network.
In summary, Storage Transfer Service (option A) is the most appropriate Google Cloud product for automating and managing the migration of terabytes of data from on-premises file servers to Google Cloud Storage using an existing Dedicated Interconnect connection.
Your organization needs to analyze data in order to gather insights into its daily operations. You only want to pay for the data you store and the queries you perform. Which Google Cloud product should your organization choose for its data analytics warehouse?
A. Cloud SQL
B. Dataproc
C. Cloud Spanner
D. BigQuery
To perform data analytics, gathering insights into daily operations while only paying for the data stored and the queries performed, the most appropriate Google Cloud product is:
D. BigQuery
Explanation:
- BigQuery: BigQuery is a fully-managed, serverless, and highly scalable data warehouse designed for analyzing large datasets. With BigQuery, you only pay for the data you store and the queries you run, making it cost-effective and suitable for analytical workloads. It’s optimized for running fast SQL queries over large datasets and provides real-time insights into your data.
- Cloud SQL (A): Cloud SQL is a managed relational database service, suitable for traditional transactional applications. It’s not designed for data analytics and may not be cost-effective for large-scale analytical workloads.
- Dataproc (B): Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters. It’s suitable for big data processing and analysis but requires cluster management and may not provide the same serverless and cost-effective model as BigQuery.
- Cloud Spanner (C): Cloud Spanner is a globally distributed, horizontally scalable, and strongly consistent relational database service. It’s designed to provide transactional consistency at global scale and may not be the most cost-effective choice for data analytics.
In summary, for cost-effective data analytics with a pay-as-you-go model, where you only pay for the data stored and the queries performed, BigQuery (option D) is the most suitable Google Cloud product for building a data analytics warehouse.
Your organization wants to run a container-based application on Google Cloud. This application is expected to increase in complexity. You have a security need for fine-grained control of traffic between the containers. You also have an operational need to exercise fine-grained control over the application’s scaling policies.
What Google Cloud product or feature should your organization use?
A. Google Kubernetes Engine cluster
B. App Engine
C. Cloud Run
D. Compute Engine virtual machines
To run a container-based application with fine-grained control of traffic between containers and operational control over scaling policies, the most appropriate Google Cloud product or feature is:
A. Google Kubernetes Engine (GKE) cluster
Explanation:
- Fine-Grained Traffic Control: GKE allows fine-grained control over network policies and traffic between containers using Kubernetes Network Policies. This enables you to define and enforce specific communication rules between containers within the cluster.
- Operational Control over Scaling Policies: GKE provides comprehensive control over scaling policies and strategies through Kubernetes. Kubernetes offers features like Horizontal Pod Autoscaling and Vertical Pod Autoscaling, allowing you to control scaling based on metrics such as CPU usage, memory usage, etc.
- Container Orchestration: GKE is designed to orchestrate and manage containerized applications effectively, providing features for deploying, managing, and scaling containers.
App Engine (B) is a managed platform-as-a-service (PaaS) offering, which abstracts away much of the infrastructure management, including fine-grained control over networking between containers and scaling policies. While it’s efficient for many use cases, it might not provide the level of control mentioned in the requirements.
Cloud Run (C) is a fully managed serverless platform for running stateless containers. While it offers autoscaling based on demand, it might not provide the fine-grained control over traffic between containers as requested.
Compute Engine (D) is a flexible infrastructure as a service (IaaS) option where you have full control over the virtual machines, but managing the scaling policies and traffic between containers at the fine-grained level would require significant manual configuration and may not be the most efficient choice for this scenario.
In summary, Google Kubernetes Engine (GKE) cluster (option A) is the most appropriate Google Cloud product to fulfill the requirements of fine-grained control over traffic between containers and operational control over scaling policies for your container-based application.
Which Google Cloud product or feature makes specific recommendations based on security risks and compliance violations?
A. Google Cloud firewalls
B. Security Command Center
C. Cloud Deployment Manager
D. Google Cloud Armor
The Google Cloud product or feature that makes specific recommendations based on security risks and compliance violations is:
B. Security Command Center
Explanation:
- Security Command Center: Security Command Center is a comprehensive security and risk management platform that provides insights into the security posture of your Google Cloud environment. It helps you identify and prioritize security risks and compliance violations by providing specific recommendations and actionable insights to improve your security posture.
Google Cloud firewalls (A) allow you to control incoming and outgoing traffic to your instances. However, they are more about configuring network rules and access rather than providing specific recommendations based on security risks and compliance violations.
Cloud Deployment Manager (C) is a tool for defining, deploying, and managing Google Cloud infrastructure. It’s not focused on security recommendations based on risks or compliance violations.
Google Cloud Armor (D) is a DDoS and application defense service. While it offers protection against various security threats, it does not provide specific recommendations based on security risks and compliance violations.
In summary, Security Command Center (option B) is the Google Cloud product or feature that provides specific recommendations based on security risks and compliance violations, helping you improve the security posture of your environment.
Which Google Cloud product provides a consistent platform for multi-cloud application deployments and extends other Google Cloud services to your organization’s environment?
A. Google Kubernetes Engine
B. Virtual Public Cloud
C. Compute Engine
D. Anthos
The Google Cloud product that provides a consistent platform for multi-cloud application deployments and extends other Google Cloud services to your organization’s environment is:
D. Anthos
Explanation:
- Anthos: Anthos is a modern application management platform that provides a consistent and unified way to deploy, manage, and operate applications across various environments, including on-premises, in the cloud, and across multiple clouds. It allows you to extend Google Cloud services to your organization’s environment, ensuring consistency and ease of deployment across diverse infrastructures.
Google Kubernetes Engine (A) is a managed Kubernetes service and a part of Anthos, but Anthos encompasses a broader set of capabilities beyond just Kubernetes management.
Virtual Public Cloud (B) is not a recognized Google Cloud product or service.
Compute Engine (C) is an Infrastructure as a Service (IaaS) offering by Google Cloud, providing virtual machines for various computing needs. However, it does not specifically offer a consistent platform for multi-cloud application deployments or extend Google Cloud services to other environments.
In summary, Anthos (option D) is the Google Cloud product that provides a consistent platform for multi-cloud application deployments and extends other Google Cloud services to your organization’s environment.
Your organization is developing an application that will manage payments and online bank accounts located around the world. The most critical requirement for your database is that each transaction is handled consistently. Your organization anticipates almost unlimited growth in the amount of data stored.
Which Google Cloud product should your organization choose?
A. Cloud SQL
B. Cloud Storage
C. Firestore
D. Cloud Spanner
For an application managing payments and online bank accounts with a critical requirement for consistent transaction handling and anticipating almost unlimited data growth, the most appropriate Google Cloud product is:
D. Cloud Spanner
Explanation:
- Cloud Spanner: Cloud Spanner is a globally distributed, horizontally scalable, and strongly consistent relational database service. It provides consistent transactions across the globe and is designed to handle a large amount of data while guaranteeing strong consistency. This makes it ideal for applications dealing with financial transactions that require high consistency.
- Cloud SQL (A): Cloud SQL is a fully-managed relational database service. While it provides consistency, it may not be as suitable for handling almost unlimited data growth and may not offer the same level of scalability and globally consistent transactions as Cloud Spanner.
- Cloud Storage (B): Cloud Storage is an object storage service designed for storing and retrieving any amount of data. However, it does not provide transaction handling or consistency features required for managing payments and bank accounts.
- Firestore (C): Firestore is a NoSQL document database that offers real-time updates and scalability, but it may not provide the same level of strong consistency required for critical financial transactions.
In summary, for an application managing payments and online bank accounts with a critical requirement for consistent transaction handling and anticipating almost unlimited data growth, Cloud Spanner (option D) is the most suitable Google Cloud product, offering globally distributed, highly consistent transactions across a large amount of data.
