AI-900 Practice Test 16 — 15 Questions

Question 1

A social media platform wants to automatically generate alternative text descriptions for images posted by users to improve accessibility for visually impaired users. Which Azure Computer Vision capability should be used?

Accepted Answer

Image Captioning. Image Captioning is the correct capability because it generates human-readable descriptions of image content, which directly meets the requirement to produce alternative text for accessibility. Unlike other options, it synthesizes a complete sentence describing the scene, objects, and actions, making it ideal for screen readers.

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Optical Character Recognition (OCR)

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Object Detection

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Face Detection

Question 2

A retail company wants to build a solution that automatically reads the printed text on product labels to update inventory records. The labels contain alphanumeric characters and are in various fonts and sizes. Which Azure Cognitive Service should they use?

Accepted Answer

Azure Computer Vision - OCR. Azure Computer Vision's OCR (Optical Character Recognition) API is specifically designed to extract printed text from images, handling various fonts, sizes, and alphanumeric characters. This makes it the ideal choice for reading product labels to update inventory records, as it can process the diverse label formats commonly found in retail environments.

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Azure Face Service

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Azure Form Recognizer

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Azure Video Indexer

Question 3

A security company wants to monitor a restricted area using camera feeds. The system must detect if a person is present in each video frame and draw a rectangle around each detected person. Which Azure Cognitive Services Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct capability because it identifies and locates objects (including people) within an image by drawing bounding boxes around each detected instance. This directly matches the requirement to detect persons in video frames and draw rectangles around them, which is a core function of the Object Detection API in Azure Cognitive Services Computer Vision.

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Image Analysis (Describe image)

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Optical Character Recognition (OCR)

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Face Detection

Question 4

An autonomous delivery robot uses AI to navigate sidewalks. The robot occasionally fails to detect pedestrians in low-light conditions, leading to near-collisions. The company wants to ensure the system is robust and safe before wider deployment. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Reliability and safety. The robot's failure to detect pedestrians in low-light conditions directly impacts the system's ability to operate reliably and safely. The Reliability and safety principle in Microsoft's responsible AI framework requires that AI systems perform consistently under expected conditions and fail gracefully when they cannot. Ensuring the robot can handle edge cases like low light is a core safety requirement before deployment.

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Fairness

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Privacy and security

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Transparency

Question 5

A social media platform wants to automatically generate a textual description for each user-uploaded image to assist visually impaired users. Which prebuilt Azure Computer Vision feature should they use?

Accepted Answer

B. Option B is correct because the Azure Computer Vision Image Analysis API includes a 'caption' feature that generates a human-readable textual description of an image's content. This prebuilt capability is specifically designed to assist visually impaired users by automatically producing alt-text for images, making it the ideal choice for the social media platform's requirement.

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A

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C

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D

Question 6

An autonomous driving company is developing a system that needs to understand the road scene at a granular level. For each pixel in a camera image, the system must classify whether it belongs to the road, a pedestrian, a vehicle, a traffic sign, or the sky. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image into a predefined category, such as road, pedestrian, vehicle, traffic sign, or sky. This pixel-level classification is essential for autonomous driving to understand the road scene at a granular level, enabling precise boundary detection and scene understanding.

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Image classification

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Object detection

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Optical character recognition (OCR)

Question 7

An autonomous vehicle system needs to both read the speed limit text on traffic signs and detect the presence and location of pedestrians crossing the road. Which combination of Azure Computer Vision capabilities should be used?

Accepted Answer

Optical Character Recognition (OCR) and Object Detection. The autonomous vehicle system requires two distinct capabilities: reading text from speed limit signs (OCR) and detecting the presence and location of pedestrians (Object Detection). OCR extracts text from images, while Object Detection identifies objects and provides bounding boxes around them, making option C the correct combination.

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Image Classification and OCR

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Semantic Segmentation and OCR

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Face Detection and OCR

Question 8

A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?

Accepted Answer

Optical Character Recognition (OCR) - Read API. Option B (OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.

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Image Analysis (describe image and detect objects)

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Face API

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Custom Vision (object detection)

Question 9

A retail company wants to use Azure Computer Vision to monitor product availability on shelves. They need to detect the presence and location of any product (e.g., a box, a bottle) on a shelf image, but they do not need to identify the specific product brand or type. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it identifies and locates multiple objects within an image by drawing bounding boxes around each detected item. For monitoring product availability on shelves, the company needs to know both the presence and position of products (e.g., boxes, bottles) without identifying specific brands or types, which aligns exactly with object detection's capability to output class labels (e.g., 'product') and coordinates.

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Image classification

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 10

A marketing team wants to create original images for advertisements based on text descriptions. Which Azure OpenAI Service model capability should they use?

Accepted Answer

DALL-E. DALL-E is the correct choice because it is the Azure OpenAI Service model specifically designed for generating original images from natural language text descriptions. Unlike other models in the suite, DALL-E uses a diffusion-based architecture to create photorealistic or stylized visuals based on prompt inputs, making it ideal for the marketing team's goal of producing custom advertisement imagery.

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GPT-3.5

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Codex

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Azure Speech-to-Text

Question 11

A city's traffic department wants to predict the number of cars that will cross a particular bridge each day to plan maintenance schedules. The output of the model should be a numerical value representing the estimated traffic count. Which type of machine learning task is this?

Accepted Answer

Regression. Regression is the correct type of machine learning task because the goal is to predict a continuous numerical value—the number of cars crossing the bridge each day. Unlike classification, which predicts discrete categories, regression models output a real number, making it ideal for forecasting traffic counts.

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Classification

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Clustering

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Reinforcement learning

Question 12

A quality inspection system uses cameras to examine metal parts for surface defects. The system must identify the exact location and shape of each scratch, dent, or crack. Which Azure Computer Vision capability is best suited for this?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image, allowing the system to precisely delineate the exact location, shape, and boundaries of surface defects like scratches, dents, or cracks on metal parts. This pixel-level granularity is essential for quality inspection where the geometry of each defect must be measured and analyzed.

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Image Classification

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Object Detection

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Dense Captioning

Question 13

An online retailer wants to build a recommendation system that learns from user interactions. The system suggests a product, and if the user clicks it, it receives a positive reward; if ignored, a negative reward. Over time, the system learns to make better suggestions. Which type of machine learning best describes this approach?

Accepted Answer

Reinforcement learning. Reinforcement learning is correct because the system learns by interacting with its environment (user clicks) and receiving rewards (positive for clicks, negative for ignores) to maximize cumulative reward over time. This trial-and-error feedback loop, without explicit labeled data, is the hallmark of reinforcement learning.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 14

A restaurant chain wants to build a voice-powered ordering system for its drive-through. The system must understand when a user wants to place an order, modify an existing order, or cancel an order. It also needs to extract specific details like the menu item name and quantity from the user's speech. Which Azure AI Language feature should they use to handle both intent recognition and entity extraction?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct choice because it is specifically designed to handle both intent recognition (e.g., 'place order', 'modify order', 'cancel order') and entity extraction (e.g., menu item name, quantity) from natural language utterances. CLU uses a pre-built or custom model to map user input to intents and extract detailed entities, making it ideal for a voice-powered ordering system that needs to understand complex commands.

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Custom text classification

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Key phrase extraction

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Question answering

Question 15

A retail company wants to automatically group customers into segments based on their purchasing history, age, and location without using any predefined labels. The goal is to identify distinct customer profiles for targeted marketing campaigns. Which type of machine learning approach should they use?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct approach because the company wants to group customers into segments without predefined labels. The algorithm will discover natural patterns and clusters in the data (purchasing history, age, location) on its own, which is the core characteristic of unsupervised learning.

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Supervised learning

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Reinforcement learning

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Regression