AI-900 Practice Test 39 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 2

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 3

A manufacturing company uses cameras on an assembly line to inspect products for defects such as scratches, dents, and discoloration. They need to identify the specific type of defect and its location on each product. 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 defects (like scratches, dents, or discoloration) but also localizes each defect by drawing bounding boxes around them. This meets the requirement to both classify the specific defect type and report its location on the product.

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Image classification

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Semantic segmentation

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Optical character recognition

Question 4

What is 'sentiment analysis at scale' and how does Azure AI Language handle it?

Accepted Answer

Efficiently processing large volumes of text for sentiment insights using batch APIs and multilingual support. 'sentiment analysis at scale' refers to the ability to process large volumes of text efficiently, which Azure AI Language achieves through batch APIs that allow asynchronous processing of multiple documents, and multilingual support that enables sentiment analysis across dozens of languages without requiring separate models. This capability is designed for enterprise scenarios where throughput and language diversity are critical, not just accuracy on individual texts.

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Performing sentiment analysis only on the largest datasets to maximise accuracy

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Scaling the sentiment score range to match industry-standard rating systems

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Using larger, more powerful ML models to improve sentiment accuracy on difficult text

Question 5

A data scientist trains a regression model to predict the selling price of houses. After evaluating on a test set, the data scientist wants a metric that measures the average absolute error between predicted and actual prices, expressed in the same units (dollars) as the target variable. Which evaluation metric should the data scientist use?

Accepted Answer

Mean Absolute Error (MAE). Mean Absolute Error (MAE) is the correct metric because it directly measures the average absolute difference between predicted and actual house prices, and its result is expressed in the same unit (dollars) as the target variable. This makes it intuitive for stakeholders to understand the typical prediction error in monetary terms.

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R-squared (R²)

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Root Mean Squared Error (RMSE)

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Mean Squared Error (MSE)

Question 6

A customer service team wants to analyze thousands of call transcripts to identify common complaints and understand whether customer sentiment is positive, negative, or neutral. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. The customer service team needs to identify common complaints (which requires extracting key phrases from the call transcripts) and understand sentiment polarity (positive, negative, or neutral). Azure AI Language's prebuilt key phrase extraction returns the main talking points and recurring terms, while sentiment analysis assigns a sentiment score and labels per sentence or document. Both features are available out-of-the-box without any custom training, directly meeting the stated requirements.

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Entity recognition and text translation

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Language detection and summarization

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PII detection and conversation analysis

Question 7

What is the purpose of training data labeling in computer vision model development?

Accepted Answer

Adding ground-truth annotations to training images so the model learns what to predict. Training data labeling is the process of adding ground-truth annotations (e.g., bounding boxes, segmentation masks, or class labels) to each training image. This supervised learning step provides the model with the correct answer for each example, enabling it to learn the mapping from image features to the desired output during training. Without labeled data, the model cannot be trained to recognize objects, classify scenes, or detect anomalies in computer vision tasks.

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Adding watermarks to images for copyright protection

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Compressing images to reduce storage costs during training

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Filtering out low-quality or blurry training images

Question 8

What is 'video indexer' (Azure Video Indexer) and what insights does it extract?

Accepted Answer

A service that extracts transcripts, faces, speakers, topics, and scenes from video content. Azure Video Indexer is a cloud-based service that uses AI to analyze video and audio content. It extracts rich insights such as transcripts (speech-to-text), identified faces, speaker diarization, topics, scenes, and even sentiment, making it a comprehensive media intelligence tool rather than a storage or indexing utility.

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A tool that compresses videos to reduce storage costs in Azure Blob Storage

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A database index that speeds up queries on video metadata tables

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A tool for creating video presentations from a series of images and text

Question 9

What is 'Azure AI Studio' and what can you do with it?

Accepted Answer

A unified platform for building, testing, and deploying generative AI applications with access to multiple AI models. Azure AI Studio is a unified platform that enables developers to build, test, and deploy generative AI applications. It provides access to multiple AI models from OpenAI, Meta, and other sources, along with tools for prompt engineering, content safety, and monitoring. This makes it the correct answer because it directly matches the platform's purpose for generative AI workloads.

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A video streaming platform for AI-focused training content and tutorials

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A graphic design tool powered by AI for creating marketing assets

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An IDE plugin that adds AI code completion to Visual Studio Code

Question 10

What is the 'bias-variance tradeoff' in machine learning?

Accepted Answer

The tradeoff between underfitting (high bias) and overfitting (high variance) when choosing model complexity. The bias-variance tradeoff describes the inverse relationship between underfitting (high bias, where the model is too simple to capture patterns) and overfitting (high variance, where the model is too complex and captures noise). In Azure Machine Learning, this tradeoff is managed by tuning hyperparameters like regularization strength or tree depth to balance model complexity and generalization.

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The tradeoff between model accuracy and inference speed

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The tradeoff between training data quantity and model quality

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The difference in fairness metrics between biased and unbiased model versions

Question 11

What is the purpose of Azure Machine Learning's dataset versioning?

Accepted Answer

Tracking changes to training data over time to enable reproducibility and auditing. Azure Machine Learning's dataset versioning allows data scientists to track changes to training data over time by creating immutable snapshots of datasets. This ensures reproducibility of experiments and provides an audit trail, which is critical for compliance and debugging model performance regressions.

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Encrypting datasets with different security keys for each version

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Creating multiple copies of training data in different storage regions

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Limiting which team members can access different versions of training data

Question 12

What is 'prompt injection' and why is it a security concern for AI applications?

Accepted Answer

Malicious input that overrides an AI system's instructions to hijack its behaviour. Prompt injection is a security vulnerability where an attacker crafts input that overrides or bypasses the system-level instructions (system prompt) of an AI model, causing it to behave in unintended ways. This is a critical concern because generative AI models, especially large language models (LLMs), are designed to follow instructions in the prompt, and a malicious user can inject commands that hijack the model's behavior, potentially exposing sensitive data, generating harmful content, or performing unauthorized actions. In Azure AI services, this risk is mitigated through content filtering, input validation, and the use of metaprompt protections.

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When developers inject test prompts to evaluate model performance

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The process of adding new prompts to expand a model's capability

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Accidentally sending the wrong prompt to the model due to a software bug

Question 13

A retail company wants to analyze customer purchase histories to identify natural groups of customers with similar buying patterns. They do not have predefined categories. Which type of machine learning should they use?

Accepted Answer

Unsupervised clustering. Unsupervised clustering is the correct approach because the company wants to discover natural groupings in customer purchase histories without predefined labels. Clustering algorithms, such as K-Means or DBSCAN, partition data into clusters based on feature similarity, enabling the identification of customer segments with similar buying patterns without any prior training on labeled examples.

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Reinforcement learning

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Supervised classification

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Supervised regression

Question 14

Which type of AI workload uses historical transaction data to identify potentially fraudulent transactions in real time?

Accepted Answer

Anomaly detection or classification to flag unusual transaction patterns. Anomaly detection and classification are AI techniques specifically designed to identify patterns that deviate from normal behavior. In fraud detection, historical transaction data is used to train a model that learns typical spending patterns, and then in real time, the model flags transactions that fall outside those learned patterns as potentially fraudulent.

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Image captioning to describe transaction screenshots

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Text generation to create transaction summaries

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Object detection to verify identity documents

Question 15

A retail company uses ceiling-mounted cameras to monitor shelf stock. They want an automated system that analyzes each camera image to detect if any product is missing from its expected location on the shelf (a product gap). Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it can identify and locate multiple objects (e.g., product boxes) within an image and determine if expected items are missing from their designated positions on the shelf. Unlike image classification, which assigns a single label to the entire image, object detection provides bounding boxes and class labels for each detected object, enabling precise gap analysis.

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Image classification

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Optical Character Recognition (OCR)

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Face detection