AI-900 Practice Test 2 — 25 Questions

Question 1

A museum wants to create an app that allows visitors to take a photo of a painting and receive information about the artist, year, and style. The app needs to identify the painting from a database of thousands of artworks. Which Azure Computer Vision capability is most suitable?

Accepted Answer

Image classification. Image classification is the correct choice because the app needs to assign a single label (the specific painting) to the entire photo. Azure Computer Vision's image classification models are trained to recognize and categorize entire images into predefined classes, which matches the requirement of identifying a painting from a database of thousands of artworks based on the visual content of the photo.

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Optical Character Recognition (OCR)

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Object detection

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Face detection

Question 2

A museum wants to automatically generate descriptive tags for its digital art collection. They need to identify objects, themes, and artistic styles in the images without any custom training. Which Azure Computer Vision feature should they use?

Accepted Answer

Azure AI Computer Vision Image Analysis. Azure AI Computer Vision Image Analysis provides pre-built models that can automatically generate descriptive tags for images, identifying objects, themes, and artistic styles without any custom training. This feature uses a set of thousands of recognizable objects, living beings, scenery, and actions, making it ideal for the museum's requirement to tag digital art without custom model development.

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Azure AI Custom Vision

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Azure AI Face service

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Azure AI Form Recognizer

Question 3

A medical research team needs to analyze CT scans to identify and outline the exact boundaries of lung nodules. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct capability because it classifies each pixel in an image, enabling precise delineation of object boundaries. For CT scans, this allows the model to outline the exact shape and contour of lung nodules, which is essential for medical analysis. Image classification and object detection only provide labels or bounding boxes, not pixel-level boundaries.

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Image Classification

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Object Detection

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Optical Character Recognition (OCR)

Question 4

What is opinion mining (also called aspect-based sentiment analysis) in Azure AI Language?

Accepted Answer

Identifying sentiment toward specific aspects or topics mentioned in text. Opinion mining, also known as aspect-based sentiment analysis, in Azure AI Language goes beyond general sentiment to identify sentiment (positive, negative, neutral, or mixed) toward specific aspects or topics mentioned in the text. For example, in a product review like 'The battery life is great but the screen is too dim,' it would detect positive sentiment toward 'battery life' and negative sentiment toward 'screen.' Option B correctly captures this core functionality.

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Identifying who expressed an opinion in a text

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Translating opinions from one language to another

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Detecting politically biased content in news articles

Question 5

What is Azure Machine Learning?

Accepted Answer

A cloud platform for building, training, deploying, and monitoring ML models. Azure Machine Learning is a comprehensive cloud-based platform that provides end-to-end capabilities for the machine learning lifecycle, including building, training, deploying, and monitoring models. It supports various frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and offers features like automated ML, pipelines, and MLOps integration. This distinguishes it from pre-built AI services or specialized infrastructure offerings.

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A pre-built AI service for specific tasks like vision or language

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A database service optimized for storing ML training data

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A GPU-only service for deep learning training

Question 6

What is 'top_p' (nucleus sampling) in Azure OpenAI and how does it differ from temperature?

Accepted Answer

Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature. Option B is correct because top_p (nucleus sampling) in Azure OpenAI controls diversity by selecting tokens from the smallest set whose cumulative probability exceeds the threshold p, rather than sampling from the full probability distribution. This differs from temperature, which scales the logits before the softmax to flatten or sharpen the distribution; top_p dynamically cuts off the long tail of low-probability tokens, providing an alternative method to control randomness without affecting the relative ranking of high-probability tokens.

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The maximum percentage of the context window used for generating output

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The probability threshold above which the model considers a response correct

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A parameter setting the minimum confidence before the model outputs a response

Question 7

A retail chain wants to automatically detect which specific products are missing from store shelves by analyzing images from in-store cameras. Each product has a distinct shape and label. Which Azure Computer Vision capability is most appropriate for this task?

Accepted Answer

B) Object Detection. Object Detection (Option B) is the correct choice because it can identify and locate multiple products within an image by drawing bounding boxes around each detected object. This allows the system to determine which specific products are missing by comparing detected items against an expected inventory list. Image Classification would only label the entire image, not individual products, while OCR focuses on text extraction and Facial Recognition identifies people.

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A) Image Classification

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C) Optical Character Recognition (OCR)

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D) Facial Recognition

Question 8

A retail chain wants to analyze in-store security camera feeds to count the number of customers entering the store each hour. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it can identify and locate multiple instances of 'person' objects within each video frame, then track and count them over time to determine the number of customers entering per hour. Image classification only labels the entire image with a single category, which cannot provide per-object counts or spatial locations needed for accurate customer counting.

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Image classification

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Optical Character Recognition (OCR)

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Facial recognition

Question 9

A retail store uses security cameras to analyze customer behavior. They need to detect when a person enters a specific zone (e.g., an aisle) and count how many people are in that zone at any given time. Which Azure Computer Vision capability should they use?

Accepted Answer

Spatial Analysis. Spatial Analysis is the correct Azure Computer Vision capability because it is specifically designed to analyze video feeds from cameras to detect people entering predefined zones, track their movement, and count occupancy in real time. This capability uses AI models to understand spatial relationships and events within a video frame, such as a person crossing a line or entering a zone, which directly matches the requirement to detect when a person enters a specific aisle and count how many people are in that zone.

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Object Detection

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Image Classification

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Optical Character Recognition (OCR)

Question 10

A medical research organization needs to process thousands of clinical trial documents to automatically extract specific medical entities such as diseases, symptoms, medications, and dosages. They want to use a prebuilt Azure AI Language capability that is already trained on medical domain data. Which Azure AI Language feature should they use?

Accepted Answer

Text Analytics for Health. Text Analytics for Health is a prebuilt Azure AI Language capability specifically trained on medical domain data, enabling extraction of entities like diseases, symptoms, medications, and dosages from clinical trial documents without requiring custom model training. It is designed for healthcare and life sciences use cases, making it the correct choice for processing thousands of clinical documents automatically.

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Key Phrase Extraction

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Named Entity Recognition (NER)

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Custom Text Classification

Question 11

A museum wants to automatically generate detailed descriptions of artwork for a mobile app. For each painting, the app should produce a natural-language description that includes the dominant colors, the objects present in the scene, and whether the scene is indoor or outdoor. Which Azure Computer Vision capability is best suited for this task?

Accepted Answer

Image Analysis (Describe Image / Dense Captions). Image Analysis with the Describe Image or Dense Captions API is specifically designed to generate human-readable sentences summarizing the content of an image, including dominant colors, objects, and scene attributes like indoor/outdoor. This capability uses pre-trained deep learning models to produce natural-language descriptions, making it the ideal choice for the museum's requirement of detailed, automated artwork descriptions.

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Optical Character Recognition (OCR)

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Face API

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Object Detection

Question 12

A news agency needs to automatically process thousands of news articles and extract specific structured information: the publication date, the author's name, and the headline. None of these fields are standard predefined entities in Azure AI Language. Which Azure AI Language feature should they use to train a model to extract these custom entities?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model to extract domain-specific entities—such as publication date, author name, and headline—that are not part of Azure's predefined entity catalog. By providing labeled examples, Custom NER learns to identify these custom fields from unstructured text, enabling automated extraction from thousands of news articles.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 13

A retail company uses security cameras to monitor shelves. They want to identify whether a customer is holding a specific product (e.g., a green detergent bottle) and also determine the location of that product within the camera frame. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of a specific product (like a green detergent bottle) in an image but also returns bounding box coordinates that indicate the product's location within the camera frame. This dual output—classification plus localization—directly matches the requirement to both recognize the object and determine its position.

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Image classification

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Optical character recognition (OCR)

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Semantic segmentation

Question 14

What is 'grounding' in the context of Azure OpenAI and Retrieval-Augmented Generation?

Accepted Answer

Anchoring model responses to specific, retrieved source documents to improve factual accuracy. Grounding in Azure OpenAI and Retrieval-Augmented Generation (RAG) refers to the practice of anchoring the model's responses to specific, retrieved source documents. This ensures that the generated output is factually accurate and verifiable, reducing the risk of hallucination by constraining the model to use only the provided context.

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Connecting the model to electrical ground to prevent static during training

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The process of converting floating-point weights to integer values for deployment

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Setting the baseline performance metrics before model fine-tuning begins

Question 15

What is 'face detection' vs 'face identification' in Azure AI Vision?

Accepted Answer

Detection locates faces and returns attributes; identification matches faces to a known person database. Option B is correct because face detection in Azure AI Vision locates human faces in an image and returns attributes such as bounding box coordinates, landmarks (e.g., eyes, nose), and optional attributes like age or emotion. Face identification, part of the Azure Face API, goes a step further by matching a detected face against a secured person database (PersonGroup) to verify or recognize a specific individual. This distinction is fundamental: detection finds faces, identification assigns an identity.

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Face detection and identification are the same feature with different names

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Detection works on live video; identification works only on still images

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Face detection requires a paid tier; identification is available in the free tier

Question 16

What does 'responsible AI' mean in the context of Microsoft's AI principles?

Accepted Answer

Following principles of fairness, reliability, privacy, inclusiveness, transparency, and accountability in AI systems. Option B is correct because Microsoft's responsible AI framework is built on six core principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles guide the development and deployment of AI systems to ensure they are ethical, trustworthy, and beneficial to society. The other options either misrepresent the scope of responsible AI or focus on narrow compliance or access restrictions.

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Using AI only for tasks that generate a financial return on investment

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Ensuring AI models comply with GDPR data residency requirements

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Limiting AI access to only trained professionals to prevent misuse

Question 17

What is 'named entity recognition' (NER) in Azure AI Language?

Accepted Answer

Identifying and classifying real-world entities (people, organisations, locations) mentioned in text. Named entity recognition (NER) is a feature of Azure AI Language that identifies and categorizes real-world entities such as people, organizations, locations, dates, and quantities within unstructured text. It uses pre-trained machine learning models to extract these entities, enabling downstream tasks like information retrieval and content summarization. Option B correctly describes this core functionality.

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Renaming database fields to follow a consistent naming convention

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Recognising the named author of a document for copyright purposes

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Detecting when a user provides their name in a chatbot conversation

Question 18

A meeting transcription service needs to convert multilingual audio recordings into accurate text in real time. Which Azure OpenAI Service model is specifically designed for this task?

Accepted Answer

Whisper. Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.

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GPT-4

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DALL-E 2

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Codex

Question 19

A multinational corporation deploys an AI-powered language translation system that performs well for English, Spanish, and French, but has significantly lower accuracy for Swahili and Navajo. The company wants to ensure the system serves all users equitably. Which Microsoft responsible AI principle is most directly relevant to this scenario?

Accepted Answer

Inclusiveness. The correct answer is A. Inclusiveness. This principle directly addresses designing AI systems that work well for all users, including those with diverse languages, abilities, and cultural backgrounds. The scenario highlights a performance disparity for Swahili and Navajo, which are underrepresented in the training data, making inclusiveness the most relevant principle to ensure equitable service.

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Fairness

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Reliability and safety

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Transparency

Question 20

A photo sharing app wants to automatically generate descriptive captions for uploaded photos to improve accessibility for visually impaired users. Which Azure Computer Vision feature should they use?

Accepted Answer

Describe Image (Image Captioning). Option D is correct because the Describe Image (Image Captioning) feature of Azure Computer Vision generates human-readable captions that describe the content of an image. This directly meets the requirement of automatically generating descriptive captions for uploaded photos to improve accessibility for visually impaired users.

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Optical Character Recognition (OCR)

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Object Detection

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Image Classification