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Azure AI Lists 01 Flashcards

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1
Q

Machine learning model types

A
  • Anomaly Detection
  • Classification
  • Clustering
  • Regression
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2
Q

Steps in the machine learning process

A
  • Prepare data
  • Train model
  • Evaluate performance
  • Deploy a predictive service
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3
Q

Metrics for evaluating regression models

A
  • Mean absolute error (MAE)
  • Root mean squared error (RMSE)
  • Relative absolute error (RAE)
  • Relative squared error (RSE)
  • Mean zero one error (MZOE)
  • Coefficient of determination (R-squared or R2)
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4
Q

Metrics for evaluating classification models

A
  • Accuracy
  • Precision
  • Recall
  • F1 score
  • Area Under Curve (AUC)
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5
Q

AutoML machine learning operations

A
  • Classification
  • Regression
  • Time series forecasting
  • Natural language processing (NLP)
  • Computer vision
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6
Q

Responsible AI principles

A
  • Fairness
  • Reliability and Safety
  • Privacy and Security
  • Inclusiveness
  • Accountability
  • Transparency
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7
Q

Responsible conversational AI factors

A
  • Bot logic
  • Speech capture
  • Speech synthesis
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8
Q

Cognitive Service main components

A
  • Anomaly Detector
  • Cognitive Search
  • Cognitive Service for Language
  • Cognitive Service for Vision
  • OpenAI
  • Speech
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9
Q

Cognitive Service for Language components

A
  • Conversational Language Understanding
  • Language Understanding (LUIS)
  • Text Analytics
  • Translator
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10
Q

Cognitive Services for Vision

A
  • Computer Vision
  • Custom Vision
  • Face
  • Form Recognizer
  • Video Indexer
  • Azure Spacial Analysis
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11
Q

Cognitive Search features

A
  • Data from any source
  • Full text search and analysis
  • AI powered search
  • Multi-lingual
  • Geo-enabled
  • Configurable user experience
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12
Q

Cognitive Search built-in skills

A

Natural language processing skills:
* Key Phrase Extraction
* Text Translation Skill

Image processing skills:
* Image Analysis Skill
* Optical Character Recognition Skill

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13
Q

Cognitive Search projection types

A
  • Table
  • Object (JSON docs)
  • File (JPG images)
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14
Q

Cognitiive Search objects created

A
  • Data Source
  • Index
  • Indexer
  • Skillset
  • Knowledge store
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15
Q

Cognitive Search for Language scenarios

A
  • Extract information
  • Summarize text-based content
  • Classify Text
  • Answer questions
  • Understand conversations
  • Translate text
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16
Q

Cognitive Search model components

A
  • Utterances
  • Entities
  • Intents
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17
Q

Cognitive Search entity types

A
  • Machine-learned
  • List
  • RegEx
  • Pattern.any
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18
Q

Default features of a Language Service resource

A
  • Sentiment analysis
  • Key phrase extraction
  • Pre-built question answering
  • Conversational language understanding
  • Named entity recognition
  • Text summarization
  • Text analytics for health
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19
Q

Custom Language Service features

A
  • Custom question answering
  • Custom text classification
  • Custom named entity recognition
  • Custom summarization
  • Custom sentiment analysis
  • Custom text analytics for health
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20
Q

Text Analytics scenarios

A
  • Identify and categorize important concepts
  • Identify the main points in unstructured text
  • Better understand customer perception
  • Process unstructured medical data
  • Create a conversational layer over data
  • Automate workflow
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21
Q

Image classification evaluation metrics

A
  • Precision
  • Recall
  • Average precision (AP)
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22
Q

Cognitive services for face analysis

A
  • Computer Vision
  • Video Indexer
  • Face
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23
Q

Attributes generated by the Face service

A
  • Bounding box
  • Age
  • Gender
  • Emotion
  • Glasses
  • Hair
  • Facial hair
  • Makeup
  • Smile
  • Occlusion
  • Blur
  • Exposure
  • Noise
  • Head pose
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24
Q

Spacial Analysis scenarios

A
  • People counting
  • Entrance counting
  • Social distancing and face mask detection
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25
Speech recognition model types
* Acoustic model * Language model
26
Metric for evaluating clustering models
* Average distance to cluster center
27
Machine learning assets in a workspace
* Compute * Datasets * Pipelines * Experiments * Models * Endpoints
28
Compute targets for machine learning
* Compute instance * Compute cluster * Inference cluster * Attached compute
29
Languages used in Visual Studio Code for machine learning
* .Net * R * Python (Jupyter notebooks)
30
Machine Learning studio supported approaches
* Automated machine learning * Python using Jupyter notebooks * Visual drag-and-drop designer
31
Machine learning workspace data stores
* Data used for training and evaluating models * Files such as logs and output files
32
Machine Learning studio data sources
* Files uploaded from a local computer * A datasource associated with the workspace * Files accessed via an HTTP URL * Open Datasets
33
Machine Learning studio dataset types
* Tabular * File
34
Machine Learning studio tabular file types
* CSV * TSV * Parquet * JSON * Output of a SQL query
35
Categories of options for publishing a real-time inferencing model
* Web service running in a Docker container * Open Neural Network Exchange (ONNX) platforms
36
Options for publishing a real-time inferencing model as a web service running in a Docker container
* Azure Kubernetes Service (AKS) cluster * Azure Container Instances (ACI) * Azure IoT Edge
37
Steps for creating an AutoML run
1. Select a dataset 1. Name the experiment 1. Select the compute for training 1. Select the label 1. Select the model type (classification, regression, or time-series forecasting) 1. Select the primary metric 1. Set the training time
38
Machine Learning designer languages
* Python * R
39
Steps for using a trained model in a real-time inference pipeline
* Replace the dataset with a Web Service Input module. * Replace the evaluation steps with a Web Service Output module.
40
Categories of Cognitive Services
* Decision * Language * Speech * Vision * Web search
41
Cognitive Services for Decision
* Anomaly Detector * Metrics Advisor * Content Moderator * Azure Personalizer
42
Cognitive Services for Language
* Immersive Reader * Language Understanding (LUIS) * Conversational Language Understanding * Text Analytics * Translator
43
Cognitive Services for Speech
* Speech to Text * Text to Speech * Speech Translation * Speaker Recognition
44
Cognitive Services for Web Search (unclear: services have changed)
* Bing Web Search (maybe) * Cognitive Search (no longer part of Cognitive Service)
45
Image classification capabilities
* Describe an image * Categorize an image * Tag an image (object detection)
46
Object detection capabilities
* Detect common objects * Tag visual features * Detect faces (including age and sex) * Identify brands and products * Identify landmarks
47
OCR capabilities
* Extract printed text * Extract handwritten text
48
Facial detection and recognition capabilities
* Detect faces * Analyze facial features * Recognize faces * Identify famous people * Detect eyeglasses and goggles * Detect beards * Identify emotions
49
Form Recognizer guidelines for best results
* Images must be JPEG, PNG, BMP, PDF, or TIFF formats. * File size must be less than 50 MB. * Image size between 50 x 50 pixels and 10,000 x 10,000 pixels. * For PDF documents, no larger than 17 inches x 17 inches.
50
Face service guidelines for best results
* Image Format: JPEG, PNG, GIF, or BMP. * File Size: 6 MB or smaller. * Face Size Range: From 36 x 36 up to 4096 x 4096. * No extreme angles (frontal view) * No occlusion (objects blocking face)
51
Visual features from the Analyze operation
* Adult (pornographic, racy, or gory) * Brands * Categories (86 categories) * Color * Description * Faces (coordinates, sex, age) * ImageType (clipart, line drawing, etc.) * Objects * Tags (list of words) * Celebrities * Landmarks
52
Computer Vision supported files
* Less than 4MB * Greater than 50x50 pixels * JPEG, PNG, GIF, or BMP
53
OCR models
* Read (images and PDFs, async) * OCR (older, images only, synchronous)
54
Computer vision operations/capabilities
* Analyze image * Describe image * Detect objects * Generate content tags * Identify domain-specific content (celebrities and landmarks) * Generate thumbnails * OCR * Moderate content
55
Image classification domains
* General * General [A1] * General [A2] * Food * Landmarks * Retail * Compact domains
56
Object detection domains
* General * General [A1] * Logo * Products on shelves * Compact domains
57
Facial recognition operations
* Verify * Identify * Find Similar * Group
58
Form Recognizer pre-trained models
* Business cards * Invoices * Receipts
59
Text Analytics technique categories
* Analysis of text * Language modeling * Analysis of speech * Translation
60
Text analytics techniques
* Tokenization * Statistics * Frequency * Part of speech tagging (PosTag) * Sentiment analysis * Language detection
61
Language modeling techniques
* Semantic modeling * Named entity recognition (NER) * Topic detection
62
Language features for extracting information
* Extract key phrases * Find linked entities * Named Entity Recognition (NER) * Custom Named Entity Recognition (custom NER) * Personally Identifiable Information (PII) detection * Personal Health Information (PHI) detection * Text analytics for health * Custom text analytics for health
63
Language features for summarizing text
* Document summarization * Conversation summarization
64
Language features for classifying text
* Analyze sentiment and mine text for opinions * Detect language * Custom text classification
65
Language features for understanding conversations
* Conversational language understanding * Orchestration workflow
66
Major NLP workloads
* Language modeling * Key phrase extraction * Named entity recognition (NER) * Sentiment analysis * Speech recognition and synthesis * Translation
67
Named entity recognition (NER) entity categories
* Person (name) * PersonType (job) * DateTime * Quantity (dimensions, age, temperature, etc.) * Location (landmark, building, city) * Organization * Event * Product * Skill * Address * Phone number * Email * URL * IP address
68
Language Understanding (LUIS) prebuilt domains
* Calendar * Communication * Email * HomeAutomation * Notes * Places * RestaurantReservation * ToDo * Utilities * Weather * Web
69
Language Understanding (LUIS) prebuilt entities
* Age * Currency (money) * DatetimeV2 * Dimension * Email * GeographyV2 * KeyPhrase * Number * Ordinal * OrdinalV2 * Percentage * PersonName * Phonenumber * Temperature * URL
70
Bot tools
* Bot Framework Composer (build bots) * Bot Framework Emulator (test bots locally)
71
Bot lifecycle steps
* Plan * Build * Test * Publish * Connect to channels * Evaluate

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