AI-900 Practice Test 18 — 15 Questions

Question 1

A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it identifies and localizes multiple objects within an image by drawing bounding boxes around each detected instance. In this scenario, the system needs to locate and count individual boxes on shelves, which requires both classification (what is a box) and localization (where each box is), exactly what object detection provides.

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Image classification

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Semantic segmentation

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Optical Character Recognition (OCR)

Question 2

A retail company wants to segment its customers into different groups based on purchasing behavior, without using predefined categories. Which type of machine learning task should they use?

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique that groups data points based on inherent similarities without requiring predefined labels. In this scenario, the retail company wants to discover natural segments in customer purchasing behavior, such as high-frequency buyers or discount seekers, without providing any existing categories. Azure Machine Learning offers clustering algorithms like K-Means, which iteratively assigns customers to clusters by minimizing within-cluster variance based on features like purchase frequency and average order value.

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Classification

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Regression

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Reinforcement learning

Question 3

A writer uses Azure OpenAI Service to generate multiple story ideas. They find that the model often repeats the same concepts across different outputs. Which parameter should they increase to reduce repetition and encourage more novel content?

Accepted Answer

Frequency penalty. The frequency penalty parameter in Azure OpenAI Service reduces the likelihood of repeating the same tokens or phrases by applying a penalty proportional to the frequency of tokens already generated. Increasing this value discourages the model from reusing common concepts, thereby promoting more novel and diverse story ideas.

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Temperature

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Top_p (nucleus sampling)

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Max tokens

Question 4

What is 'semantic search' in Azure AI Search (cognitive search)?

Accepted Answer

Search that understands the meaning and intent of queries to return conceptually relevant results. Semantic search in Azure AI Search uses advanced AI models to understand the meaning and intent behind a user's query, rather than relying solely on keyword matching. It re-ranks search results based on conceptual relevance to the query, enabling the system to return results that are semantically related even if they don't contain the exact keywords. This is powered by Azure's deep learning models, including transformer-based language models, to capture the context and semantics of the search terms.

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A search that finds all documents containing the exact keywords typed by the user

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Searching for programming code by its semantic meaning in a code repository

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Restricting search results to documents tagged with specific metadata labels

Question 5

What is a 'system message' (system prompt) in Azure OpenAI chat models?

Accepted Answer

A developer-set instruction that defines the model's role, persona, and behavioural constraints. Option B is correct because a system message (system prompt) in Azure OpenAI chat models is a developer-defined instruction that sets the model's role, persona, and behavioral constraints. This prompt is sent as part of the conversation context to guide the model's responses, ensuring it adheres to specific guidelines, tone, or safety rules. It is not an error notification, automated confirmation, or user input.

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An error notification sent by Azure when the OpenAI service is unavailable

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Automated messages the model sends to confirm it received the user's input

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The first message a user sends to start a new conversation session

Question 6

An e-commerce website wants to automatically remove the background from product photos uploaded by sellers so that items appear on a consistent plain background. Which Azure Computer Vision capability should they use?

Accepted Answer

Background Removal. Background Removal is the correct capability because it is specifically designed to isolate the foreground subject from the background in an image, producing a transparent or solid-color background. This directly meets the requirement of automatically removing backgrounds from product photos to create a consistent plain background. Azure's Background Removal API uses deep learning models trained on millions of images to segment the primary object from its surroundings.

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Optical Character Recognition (OCR)

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Image Captioning

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Object Detection

Question 7

A social media company uses Azure OpenAI Service to automatically generate captions for user-uploaded images. The company has a strict content policy that prohibits any generated captions containing profanity, hate speech, or self-harm references. Which feature of the Azure OpenAI Service should the company configure to automatically block such harmful content?

Accepted Answer

Content filtering. Content filtering is the correct feature because it is specifically designed to detect and block harmful content such as profanity, hate speech, and self-harm references in both input prompts and generated outputs. Azure OpenAI Service's content filtering system uses multi-class classification models to enforce responsible AI policies automatically, without requiring custom training or manual moderation.

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Temperature parameter

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Top-p parameter

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Max-tokens parameter

Question 8

An e-commerce company has a dataset of customer purchase histories with no predefined categories. The data analyst wants to identify natural groupings of customers based on their purchasing behavior to target marketing campaigns. Which type of machine learning should the analyst use?

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique used to discover inherent groupings in data without predefined labels. In this scenario, the analyst wants to identify natural customer segments based on purchase behavior, which aligns perfectly with clustering algorithms like K-Means or DBSCAN that partition data into clusters of similar patterns.

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Regression

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Classification

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Reinforcement learning

Question 9

A city transportation department wants to use a live camera feed at a bus stop to estimate how many people are waiting for the bus. Which Azure Computer Vision capability should they use?

Accepted Answer

C. Object detection. Object detection is the correct capability because it can identify and locate multiple people in a live camera feed, providing bounding boxes around each person. This allows the system to count the number of individuals waiting at the bus stop, which is the core requirement. Optical Character Recognition (OCR) extracts text, face detection identifies faces but not necessarily counts people in a crowd, and semantic segmentation classifies each pixel but is overkill for simple counting.

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A. Optical Character Recognition (OCR)

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B. Face detection

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D. Semantic segmentation

Question 10

A software company uses Azure OpenAI to generate code snippets. They want to evaluate how confident the model is in each token it generates. Which Azure OpenAI feature provides a numerical measure of confidence for each generated token?

Accepted Answer

Logprobs. Logprobs (log probabilities) is the Azure OpenAI feature that provides a numerical measure of confidence for each generated token. It outputs the log probability of each token being selected by the model, allowing developers to assess how certain the model is about its predictions at the token level.

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Temperature

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Top-p

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Presence penalty

Question 11

An e-commerce company deploys an AI-powered robot for warehouse inventory management. The robot uses computer vision to navigate and pick items. In certain lighting conditions, the robot misidentifies empty shelves and attempts to pick items that are not there, causing damage. According to Microsoft's Responsible AI principles, which principle is most directly concerned with ensuring the robot performs correctly and safely under expected conditions?

Accepted Answer

Reliability and Safety. The robot's failure to perform correctly under varying lighting conditions directly violates the Reliability and Safety principle, which mandates that AI systems must operate consistently and safely within their defined operational parameters. This principle requires rigorous testing across expected environmental conditions (e.g., lighting variations) to prevent physical damage and ensure predictable behavior.

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Fairness

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Privacy and Security

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Transparency

Question 12

A warehouse deploys cameras to automatically process incoming packages. The system must read the serial numbers printed on each package label to update inventory records. The labels often have varied fonts and sizes, and may be slightly rotated. Which Azure Computer Vision capability should be used to extract the serial numbers?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct Azure Computer Vision capability because it is specifically designed to extract printed or handwritten text from images, including serial numbers with varied fonts, sizes, and rotations. Azure's OCR API (part of Computer Vision) can handle skewed or rotated text by automatically detecting and correcting orientation before recognizing characters, making it ideal for warehouse labels that are not perfectly aligned.

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Object detection

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Image classification

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Facial recognition

Question 13

A university uses an AI system to screen scholarship applications. The system was trained on historical data that mostly awarded scholarships to students from STEM majors. Consequently, the system consistently gives lower scores to equally qualified students from humanities and arts majors. Which Microsoft responsible AI principle is most directly being violated by this outcome?

Accepted Answer

Fairness. The AI system's training data caused it to learn a biased pattern that systematically disadvantages humanities and arts applicants, which directly violates the fairness principle. Fairness in responsible AI requires that systems treat all groups equitably and do not perpetuate or amplify existing biases, especially when making high-stakes decisions like scholarship awards.

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Reliability and safety

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Privacy and security

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Transparency

Question 14

A robotics team is training a robot to navigate a maze. The robot receives a positive reward (+10) when it reaches the exit and a negative reward (-1) every time it bumps into a wall. The robot learns to maximize its cumulative reward over multiple trials. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. The robot learns by interacting with its environment, receiving rewards (positive for reaching the exit, negative for bumping into walls), and adjusting its behavior to maximize cumulative reward over time. This trial-and-error learning process, where an agent learns a policy through feedback from its actions, is the defining characteristic of reinforcement learning.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 15

A data scientist trains a multiclass classification model to categorize customer support tickets into three types: 'Billing', 'Technical', and 'General'. The dataset contains 80% 'General', 15% 'Billing', and only 5% 'Technical' tickets. Overall accuracy on a test set is 85%, but the model misclassifies most 'Technical' tickets as 'General'. Which metric would best help the data scientist understand the model's poor performance on the 'Technical' class?

Accepted Answer

F1-score for the 'Technical' class. The F1-score for the 'Technical' class is the best metric because it combines precision and recall into a single harmonic mean, directly capturing the model's inability to correctly identify the minority class. Since the dataset is heavily imbalanced (only 5% 'Technical'), overall accuracy (85%) is misleadingly high, as the model can achieve it by simply predicting the majority class 'General'. The F1-score penalizes both false positives and false negatives, making it the standard metric for evaluating classifier performance on imbalanced classes.

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Overall accuracy

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Confusion matrix

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Precision for the 'General' class