AI-900 Practice Test 17 — 15 Questions

Question 1

A retail store wants to use an AI solution to automatically monitor security camera feeds and detect when a shelf is empty or if a person is in a restricted area. Which type of AI workload is best suited for this task?

Accepted Answer

Computer Vision. Computer Vision is the correct AI workload because it enables the system to analyze video frames from security cameras to detect visual patterns such as empty shelves (object absence) or unauthorized persons in restricted areas (object presence and location). This workload uses image classification, object detection, and semantic segmentation to interpret visual data in real time.

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Natural Language Processing

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Speech Recognition

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Anomaly Detection

Question 2

A retail company wants to automatically analyze thousands of product reviews to identify the most frequently mentioned aspects, such as 'battery life', 'screen quality', and 'customer service'. They plan to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.

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Text Analytics for Health

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Entity linking

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Sentiment analysis

Question 3

A retail company uses an AI system to predict customer churn based on demographic and behavioral data. The team discovers that the model gives disproportionately higher churn predictions for customers from a particular zip code, even when their behavior is similar to others. Which Microsoft responsible AI principle is most directly relevant to addressing this issue?

Accepted Answer

Fairness. The model's disproportionate churn predictions for a specific zip code, despite similar behavior, indicates a bias that unfairly impacts that group. Microsoft's Fairness principle directly addresses this by requiring AI systems to treat all groups equitably and avoid discrimination based on sensitive attributes like location. Ensuring fairness involves auditing training data and model outputs for such disparities and applying mitigation techniques.

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Transparency

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Reliability and Safety

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Privacy and Security

Question 4

A robotics company is training a drone to fly autonomously through an obstacle course. The drone receives positive rewards for staying on course and avoiding obstacles, and negative rewards for collisions. The system learns by trial and error to maximize its cumulative reward. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. Reinforcement learning is the correct choice because the drone learns by interacting with its environment, receiving rewards (positive for staying on course, negative for collisions), and adjusting its behavior through trial and error to maximize cumulative reward. This is the defining characteristic of reinforcement learning, where an agent learns a policy from feedback signals rather than from labeled data or hidden patterns.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 5

What is 'Azure Machine Learning's Responsible AI dashboard' and what does it include?

Accepted Answer

A unified tool for error analysis, interpretability, fairness, data exploration, and causal inference. Option B is correct because the Responsible AI dashboard in Azure Machine Learning is a unified, integrated tool that combines multiple components for building and evaluating AI systems responsibly. It includes error analysis, model interpretability, fairness assessment, data exploration, and causal inference capabilities, all accessible through a single interface. This dashboard helps data scientists and developers understand model behavior, identify potential biases, and make informed decisions throughout the ML lifecycle.

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A compliance checklist confirming a model meets Microsoft's responsible AI certification requirements

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A monitoring dashboard showing responsible AI policy violations in production

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A report auto-generated and submitted to regulators when a model is deployed

Question 6

A security company wants to use Azure Computer Vision to monitor a restricted area. They need to count the number of people present in each camera frame and draw bounding boxes around each person. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (object detection). Option B (Image Analysis with object detection) is correct because Azure Computer Vision's object detection capability can identify and locate multiple instances of a specific object class—in this case, people—within an image. It returns bounding box coordinates for each detected person, enabling the security company to count individuals and draw boxes around them in each camera frame.

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Optical Character Recognition (OCR)

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Face detection

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Image classification

Question 7

What is 'conversation summarisation' in Azure AI Language?

Accepted Answer

Generating concise summaries of dialogues capturing key points, decisions, and action items. Conversation summarization in Azure AI Language is a prebuilt feature that uses extractive and abstractive summarization techniques to generate concise summaries of dialogues, capturing key points, decisions, and action items. It is designed specifically for multi-turn conversations (e.g., customer service chats, meeting transcripts) and outputs a structured summary, not just a count of messages.

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Summarising how many messages were exchanged in a conversation

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A tool for moderators to summarise flagged content for compliance review

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Automatically creating FAQ articles from the most common chatbot conversations

Question 8

A warehouse uses AI to monitor inventory. They need to detect the presence and location of specific objects (e.g., forklifts, pallets) in real-time video feeds. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it identifies specific objects (e.g., forklifts, pallets) within an image or video frame and returns bounding box coordinates indicating their location. This capability is designed for real-time spatial awareness, which directly matches the warehouse's need to detect both the presence and position of objects in video feeds.

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Image classification

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OCR (optical character recognition)

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Facial recognition

Question 9

A retail company uses overhead cameras to monitor shelf inventory in a store. They want to build a system that automatically detects whether a shelf section is empty or stocked, and specifically identify product categories (e.g., 'soft drinks', 'chips', 'canned goods') and count the number of items in each category. The company has a large set of labeled images showing different shelf states. Which Azure Computer Vision service should they use to build this custom detection and counting solution?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it allows the company to train a model on their labeled images to detect and localize specific product categories (e.g., 'soft drinks', 'chips') and count items within each category. Unlike pre-built Computer Vision features, Custom Vision enables custom object detection with bounding boxes and classification, which directly supports the requirement for detecting shelf states and counting items per category.

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Computer Vision Image Analysis with dense captioning

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Optical Character Recognition (OCR)

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Azure Machine Learning with a pre-trained YOLO model

Question 10

A retail store wants to analyze customer movement patterns, such as dwell time in front of displays and foot traffic heatmaps, using existing surveillance cameras. Which Azure Computer Vision capability is most suitable?

Accepted Answer

Spatial analysis. Spatial analysis is the correct choice because it is specifically designed to analyze people's presence, movement, and interactions within a physical space using video feeds. It can measure dwell time in front of displays and generate foot traffic heatmaps by tracking individuals across camera frames, which directly matches the retail store's requirements.

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Object detection

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Optical character recognition (OCR)

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Image classification

Question 11

A travel agency wants to build a chatbot that can automatically answer customer questions about flight status by extracting answers from a PDF document containing FAQs. Which Azure AI Language feature should they use to directly query this content?

Accepted Answer

Question Answering. Option B, Question Answering, is correct because it is specifically designed to extract answers directly from a provided document (such as a PDF FAQ) by using a pre-built or custom knowledge base. The travel agency can upload the PDF, and the service will return precise answers to user queries without requiring intent classification or entity extraction, which is exactly what is needed for querying flight status from a static FAQ document.

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Conversational Language Understanding (CLU)

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Text Analytics for health

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Translator

Question 12

A real estate company wants to create an application that automatically generates floor plans from photographs of rooms. The application needs to identify and delineate every pixel in the image that corresponds to walls, doors, windows, and furniture. Which Azure Computer Vision capability should the company use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image into predefined categories (e.g., walls, doors, windows, furniture), producing a pixel-level mask. This is exactly what the application needs to delineate each structural element and object in the room photograph, enabling accurate floor plan generation.

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Object Detection

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Image Classification

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Optical Character Recognition (OCR)

Question 13

An autonomous vehicle company uses an AI system for navigation. During testing, the system performs well in sunny weather but fails in snowy conditions because the training data had very few examples of snowy roads. The company decides to deploy the system anyway, hoping it will learn on the road. Which Microsoft responsible AI principle is most directly violated by this decision?

Accepted Answer

Reliability and Safety. The decision to deploy an AI system that is known to fail in snowy conditions directly violates the Reliability and Safety principle. This principle requires that AI systems operate reliably and safely under all expected conditions, and that potential failures are identified and mitigated before deployment. By hoping the system will 'learn on the road,' the company is exposing users and the public to unacceptable risk, as the system has not been validated for safe operation in snowy environments.

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Fairness

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Privacy and Security

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Inclusiveness

Question 14

A warehouse uses video cameras to monitor a conveyor belt. They need to count the number of boxes passing by each hour to track throughput. 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 boxes within each video frame, allowing the system to count them as they move along the conveyor belt. Unlike image classification, which labels an entire image, object detection provides bounding boxes and counts for each detected object, making it ideal for real-time throughput tracking.

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Optical Character Recognition (OCR)

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Face Detection

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Image Classification

Question 15

A research team wants to automatically analyze thousands of online news articles to identify the main topics discussed in each article (e.g., 'technology', 'politics', 'sports'). They need a prebuilt Azure AI Language feature that returns a list of key concepts or topics without any custom training. Which feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it automatically identifies and returns a list of the main topics, concepts, or themes discussed in a document without requiring any custom training or labeled data. This prebuilt capability is designed specifically for extracting high-level topics from unstructured text, making it ideal for analyzing thousands of news articles to determine subjects like 'technology', 'politics', or 'sports'.

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Named Entity Recognition (NER)

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Sentiment analysis

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Language detection