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CAIC 8 Flashcards

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1
Q

What is the primary challenge companies face regarding ML projects?

A

Identifying business use cases for ML

Not being able to identify a business problem and its value proposition is a significant hurdle.

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2
Q

What are common data-related challenges faced by companies in ML adoption?

A

Data quality, data inventory, data accessibility, data governance, data availability

These challenges affect both data-poor and data-rich companies.

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3
Q

What is a significant human resource challenge in ML adoption?

A

Shortage of data science and ML talent

Companies struggle to attract and retain top ML talents across all industries.

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4
Q

What roles are increasingly needed alongside data scientists in ML initiatives?

A

ML product management, ML infrastructure engineering, ML operations management

These roles are necessary as the complexity of ML projects increases.

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5
Q

What cultural challenge hinders the adoption of ML solutions in organizations?

A

Perception of ML as a threat to job functions

Lack of knowledge in ML contributes to hesitance in adopting new methods.

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6
Q

What does ML solutions architecture aim to address?

A

Challenges in ML adoption

It serves as a bridge connecting different components of an ML initiative.

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7
Q

What are the core functional areas covered by ML solutions architecture?

A
  • Business problem understanding and transformation using AI and ML
  • Identification and verification of ML techniques
  • System architecture design and implementation
  • ML platform automation technical design
  • Security, compliance, and audit considerations

These areas aim to ensure effective ML solution deployment.

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8
Q

What is the goal of business workflow analysis in the context of ML?

A

Identify inefficiencies and determine if ML can improve processes

This can help eliminate pain points or create new revenue opportunities.

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9
Q

What might a company do to address identified inefficiencies in a call center?

A

Analyze workflows to identify pain points, then apply ML solutions like virtual assistants, call recording analytics

Modifying business processes may also be necessary.

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10
Q

What is the focus of ML solutions architecture?

A

Identifying and applying ML algorithms to address various ML problems

It does not involve developing new machine algorithms.

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11
Q

What technical capabilities must an ML platform provide for data scientists?

A
  • Data exploration
  • Experimentation
  • Model building
  • Model evaluation

These capabilities support the different phases of the ML cycle.

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12
Q

What are key components of automation in ML platform design?

A
  • Creating automation pipelines
  • Running and monitoring pipelines
  • Monitoring model performance metrics

These components assist in managing the ML workflow effectively.

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13
Q

What security measures are essential for an ML platform?

A
  • Authentication and authorization mechanisms
  • Network security controls
  • Data encryption

These measures help prevent unauthorized access and ensure compliance.

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14
Q

What do industry technology providers offer for ML infrastructure design?

A

Best practices and architectural guidelines

For example, Amazon Web Services created Machine Learning Lens.

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15
Q

In financial services, what areas are using ML solutions?

A
  • Capital markets
  • Insurance
  • Banking

ML addresses various challenges in these sectors.

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16
Q

What is the front office in financial services?

A

The revenue-generating business area including customer-facing roles

It involves securities sales, trading, investment banking, and financial advising.

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17
Q

What are core challenges faced by sales trading professionals?

A
  • Generating accurate market insights
  • Making informed investment decisions
  • Achieving optimal trading executions

These challenges require efficient strategies and timely responses.

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18
Q

How can ML assist in the trading domain?

A
  • Discovering patterns for trading strategies
  • Estimating trading costs
  • Identifying optimal execution strategies

ML models analyze large datasets to inform trading decisions.

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19
Q

What is the primary use of ML models in trading?

A

To discover patterns to inform trading strategies such as pair trading

ML models analyze data points like company fundamentals and trading patterns.

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20
Q

What are some key activities performed by investment banking staff?

A
  • Financial modeling
  • Business valuation
  • Pitch book generation
  • Transaction document preparation

These activities are essential for executing investment banking deals.

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21
Q

What are the challenges faced by investment banking due to large amounts of data?

A

Searching and analyzing large amounts of documents and data

Junior bankers spend many hours extracting useful information manually.

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22
Q

What ML technique can help with document management in investment banking?

A

NLP for automatic entity extraction

NLP can assist in quickly finding relevant information from large volumes of text.

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23
Q

What does wealth management (WM) involve?

A

Advising clients on wealth planning and structuring to grow and preserve wealth

WM firms also offer tax planning and estate planning.

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24
Q

What challenges do wealth management firms face?

A
  • Demand for personalized financial planning strategies
  • Need for new channels of engagement
  • Coverage of more clients while maintaining service quality

Tech-savvy clients expect enhanced service options.

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25
How do WM firms utilize ML to enhance client services?
By analyzing client transaction history and preferences to recommend suitable investment products ## Footnote Models account for client likelihood of accepting offers.
26
What is the back office in financial services?
Handles critical support activities like trade settlement and regulatory compliance ## Footnote Although not client-facing, it is essential for operational efficiency.
27
What is NAV in mutual funds and ETFs?
Net Asset Value, calculated as the value of assets minus liabilities ## Footnote NAV represents the price at which investors can buy or sell the fund.
28
What are the critical steps in calculating NAV?
* Stock reconciliation * Reflection of corporate actions * Pricing the instrument * Booking and calculating fees * NAV/price validation ## Footnote Each step is essential for accurate fund valuation.
29
What is the main objective of the NAV validation step?
To detect pricing exceptions ## Footnote This is treated as an anomaly detection challenge.
30
What role do ML models play in predicting settlement failures?
They help brokers predict trade failures early in the process ## Footnote This allows for preventive actions to be taken.
31
What areas in financial services are top priorities for ML adoption?
* Fraud prevention * Risk management ## Footnote These areas have significant financial and regulatory implications.
32
What regulations require financial institutions to combat money laundering?
AML regulations ## Footnote These regulations require substantial resources for compliance.
33
What ML techniques are used in anti-money laundering (AML) efforts?
* Network link analysis * Clustering analysis * Deep learning-based predictive analytics * NLP for data gathering ## Footnote These techniques enhance the detection of criminal activities.
34
What is the purpose of trade surveillance in financial institutions?
To identify and investigate potential market abuse ## Footnote Market abuse includes insider trading and market manipulation.
35
How can trade surveillance be framed as ML problems?
* Classifying abuse detection as a classification problem * NLP for entity extraction from unstructured data * Network analysis for trader collaborations * Anomaly detection for abusive behaviors ## Footnote This allows for more effective monitoring and investigation.
36
What are the two main aspects evaluated in credit risk modeling?
* Probability of borrower default * Impact on lender's financial situation ## Footnote These factors are critical for assessing loan risks.
37
What is the underwriting process in insurance?
Evaluating risks of offering insurance coverage to individuals and assets ## Footnote This process determines appropriate premiums based on risk factors.
38
What challenges do insurance companies face in the underwriting process?
* Limited data review by human underwriters * Potential personal bias in decision-making ## Footnote These limitations can impact the accuracy of risk assessments.
39
How can ML improve the insurance claims process?
* Automating data extraction * Estimating repair costs * Detecting exceptions in claims ## Footnote This can significantly speed up claim processing and reduce manual effort.
40
What type of data do insurance companies collect during the insurance claim process?
Insurance companies collect a lot of data including: * Property details * Details and photos of damaged items * Insurance policy * Claims history * Historical fraud data ## Footnote This data is essential for processing claims and identifying potential fraud.
41
How can machine learning (ML) assist in the insurance claim process?
ML can assist by: * Automating data extraction from documents * Identifying insured objects from pictures * Estimating repair and replacement costs * Detecting exceptions in claims * Predicting potential fraud ## Footnote These applications can significantly reduce manual effort and speed up claim processing.
42
What are the key changes in the Media & Entertainment (M&E) industry due to streaming?
Key changes include: * Shift from traditional broadcasting to streaming * Increased competition * Demand for personalized and enhanced experiences * Need for new monetization channels * Improvement in operational efficiency ## Footnote The M&E industry must adapt to these changes to remain competitive.
43
What roles does machine learning play in the media lifecycle?
ML is used for: * Enhancing content management and search * Developing new content * Optimizing monetization * Enforcing compliance and quality control ## Footnote This leads to improved efficiency and business growth.
44
What is the purpose of segmenting long video content into micro-segments?
Segmenting long video content allows producers to: * Distribute content individually * Repackage content for personalized viewing ## Footnote This approach caters to individual preferences and enhances viewer engagement.
45
Why is metadata important in digital asset management?
Metadata is important because it: * Enables effective content discovery * Supports searching for diverse usages * Helps curate content for new monetization opportunities ## Footnote Without adequate metadata, discovering relevant content becomes challenging.
46
How can computer vision models assist in content tagging?
Computer vision models can automatically tag content based on: * Objects * Genres * People * Places * Themes ## Footnote This automation reduces the need for costly and time-consuming manual tagging.
47
What techniques can be used for audio content analysis?
Techniques include: * Transcribing audio into text * Summarizing long text for metadata generation ## Footnote These techniques enhance data analysis and improve content management.
48
What challenges do M&E companies face in customer acquisition and retention?
Challenges include: * Keeping users engaged * Competing with diverse content choices * Delivering highly personalized features ## Footnote To address these challenges, companies focus on enhancing user experience.
49
What is the function of a content recommendation engine?
A content recommendation engine functions by: * Using behavior data to train ML models * Targeting individuals based on preferences ## Footnote It helps in recommending a diverse range of media content to users.
50
What is the significance of the drug sector in healthcare and life sciences?
The drug sector is significant because it: * Develops medications for various illnesses * Invests heavily in research and development * Faces extensive clinical trials before market release ## Footnote This sector is critical for treating both minor and life-threatening conditions.
51
What are the main stages of drug discovery and development?
The main stages include: * Discovery and development * Preclinical research * Clinical development * FDA review * Post-market monitoring ## Footnote Each stage is crucial for ensuring drug safety and efficacy.
52
How can ML enhance the drug discovery process?
ML enhances drug discovery by: * Predicting drug efficacy and toxicity * Identifying new drug targets * Understanding protein folding ## Footnote These capabilities can accelerate development and improve safety.
53
What role does ML play in optimizing clinical trials?
ML helps optimize clinical trials by: * Identifying potential patient cohorts * Predicting trial success likelihood ## Footnote This leads to more effective trial designs and better outcomes.
54
What are the challenges associated with extracting insights from healthcare data?
Challenges include: * Manual processing being expensive and error-prone * Significant amounts of data remaining unutilized ## Footnote These issues necessitate the adoption of ML for data extraction.
55
What is the significance of the manufacturing industry?
The manufacturing industry is significant because it: * Creates a wide range of physical products * Involves multiple stages from design to post-manufacturing support ## Footnote It plays a crucial role in the economy.
56
How do AI and ML improve the manufacturing process?
AI and ML improve manufacturing by: * Forecasting sales * Predicting equipment failure * Enhancing quality control * Automating tasks ## Footnote These improvements lead to increased efficiency and reduced costs.
57
What is a benefit of using AI and ML in manufacturing?
Avoid unplanned downtime, reduces maintenance costs, and improves overall equipment effectiveness.
58
How do ML algorithms improve quality control in manufacturing?
By analyzing data from sensors and cameras to identify defective products or parts in real time.
59
What role do robots play in the manufacturing process?
Assembling products, performing quality checks, and handling material movement.
60
What is the impact of AI and ML on supply chain management?
Optimizes management, improves operational efficiency, and reduces costs.
61
What is generative design in the context of product design?
A ML technology that generates numerous design variations based on specific constraints and requirements.
62
Fill in the blank: Designers analyze customer preferences in terms of ______, texture, and style.
color
63
How can ML techniques assist in estimating the potential of new products?
By analyzing customer feedback, market trends, and competitor analysis.
64
What is the function of computer vision in quality control?
Identifying defects and flaws in manufactured products.
65
True or False: Traditional maintenance practices are always effective in preventing equipment failures.
False
66
What is predictive maintenance analytics?
ML-based analytics that forecast potential problems in equipment to prevent failures.
67
What significant change has the retail industry undergone in recent years?
The growth of e-commerce has outpaced traditional retail businesses.
68
Name one technology being used to enhance the shopping experience in retail.
Augmented reality
69
What is the purpose of visual search technology in e-commerce?
Allowing consumers to identify similar-looking products using a picture.
70
Fill in the blank: Visual search technology creates a digital representation known as ______.
encoding
71
How do retailers use ML models in marketing campaigns?
To identify potential customers and optimize messaging and incentives.
72
What is contextual advertising?
A targeted marketing technique displaying ads relevant to the content on a web page.
73
True or False: Highly personalized campaigns are based solely on demographic data.
False
74
What data is used to create individual profiles for targeted marketing?
Historical transaction data, response data to campaigns, and social media data.
75
What is the goal of using ML in user-centric targeted marketing?
To predict conversion rates and send ads to users likely to convert.
76
What role does computer vision play in contextual advertising?
Analyzing video ads to extract contextual information for appropriate ad placement.

CAIC (15 decks)

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