AI-900 Practice Test 19 — 15 Questions

Question 1

What is 'liveness detection' in Azure AI Face service?

Accepted Answer

Verifying that a face presented to a camera is a real live person, not a photo or video replay. Liveness detection in Azure AI Face service is a security feature that distinguishes between a real, live person and a spoofing attempt such as a printed photo, video replay, or a 3D mask. It analyzes subtle cues like eye blinking, skin texture, and depth to ensure the face presented to the camera is physically present and alive. This prevents unauthorized access in identity verification scenarios.

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Detecting whether a celebrity face in a photograph is still alive or deceased

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Detecting human faces in real-time video streaming from security cameras

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Monitoring whether a face recognition model remains accurate after deployment

Question 2

What is 'responsible AI by design' in the context of building Azure AI applications?

Accepted Answer

Integrating ethical AI principles and safety tools throughout the entire development lifecycle. 'responsible AI by design' means proactively embedding ethical principles—such as fairness, reliability, transparency, privacy, and accountability—into every phase of building an Azure AI application, from problem definition and data collection to deployment and monitoring. This approach aligns with Microsoft's Responsible AI Standard and is operationalized through tools like Fairlearn, Error Analysis, and the Responsible AI dashboard in Azure Machine Learning, ensuring that safety and ethical considerations are not afterthoughts but integral to the development lifecycle.

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Using only Azure-approved AI models to avoid legal liability

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Designing AI systems that only respond to pre-approved questions

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Requiring legal review before every AI model deployment

Question 3

What is 'AI system' vs 'AI model' in the context of responsible AI?

Accepted Answer

An AI model is the prediction function; an AI system includes all surrounding pipelines, interfaces, and human processes. In responsible AI, the distinction is that an AI model is the mathematical prediction function (e.g., a trained neural network or decision tree), while an AI system encompasses the model plus all surrounding components: data ingestion pipelines, inference APIs, user interfaces, monitoring, logging, and human-in-the-loop processes. This broader view is critical for governance, because ethical risks (bias, drift, transparency) often arise from the system's context, not just the model's logic.

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An AI model is software; an AI system includes the hardware it runs on

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AI systems are more accurate than individual models because they combine multiple models

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An AI model runs offline; an AI system requires internet connectivity

Question 4

What is 'content moderation' in the context of Azure OpenAI?

Accepted Answer

Automatically filtering and classifying inputs/outputs for harmful content categories. Content moderation in Azure OpenAI uses AI models to automatically scan both user prompts (inputs) and generated responses (outputs) for harmful content such as hate, violence, sexual material, and self-harm. It applies configurable severity filters (e.g., low, medium, high) to block or flag content that violates Microsoft's Responsible AI policies, ensuring safe deployment of generative AI workloads.

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Controlling how much content a user is allowed to generate per day

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Editing generated text to improve grammar and style

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Optimising prompt length to reduce token costs

Question 5

What is 'customer churn prediction' as an AI workload and what ML type does it use?

Accepted Answer

Using supervised classification to predict which customers are likely to cancel or become inactive. Customer churn prediction is a supervised machine learning workload where historical customer data (e.g., usage patterns, support interactions, billing history) is used to train a classification model. The model learns to assign a binary label (churn or not churn) to new customers, making it a supervised classification task. This directly matches option B, which correctly identifies the use of supervised classification to predict likely churners.

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Analysing customer complaints to identify the root cause of service dissatisfaction

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Detecting when a customer has already churned based on their last login date

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Using NLP to understand why customers write negative reviews before leaving

Question 6

A data scientist is training a model to predict whether a patient has a rare disease (1% prevalence). The model predicts 'no disease' for all patients and achieves 99% accuracy, but fails to identify any actual cases. Which metric would best reveal this failure?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 1% disease prevalence and a model that predicts 'no disease' for all patients, recall is 0% because zero true positives are found. Accuracy (99%) is misleading here because the model fails to detect any rare disease cases, and recall directly exposes this failure.

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Precision

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F1 score

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Mean absolute error

Question 7

Which Azure AI service detects and identifies human faces in images, including attributes like age estimate and emotion?

Accepted Answer

Azure AI Face. Azure AI Face is the correct service because it is specifically designed to detect and identify human faces in images, and it can extract attributes such as age estimates, emotions (e.g., happiness, sadness), and facial landmarks. Unlike general-purpose image analysis, Azure AI Face uses specialized face detection models and returns face rectangles along with optional attribute data.

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Azure AI Vision

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Azure AI Custom Vision

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Azure AI Video Indexer

Question 8

What is the purpose of splitting data into training, validation, and test sets in machine learning?

Accepted Answer

To evaluate model performance honestly on data it hasn't seen during training. Splitting data into training, validation, and test sets is essential for honestly evaluating a model's performance on unseen data. The training set teaches the model patterns, the validation set tunes hyperparameters and prevents overfitting, and the test set provides a final, unbiased estimate of how the model will perform on new, real-world data. This separation ensures that the model's accuracy metrics reflect its generalization ability rather than memorization of the training data.

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To increase the total amount of data available for training

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To make training faster by using smaller datasets

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To comply with data privacy regulations

Question 9

What is 'prompt flow' in Azure AI Foundry?

Accepted Answer

A visual development tool for building, testing, and deploying LLM application pipelines. Prompt flow in Azure AI Foundry is a visual development tool that enables developers to design, test, and deploy end-to-end pipelines for large language model (LLM) applications. It provides a graph-based interface to orchestrate LLM calls, data processing, and custom logic, making it easier to build complex generative AI workflows without writing extensive code.

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A tool for managing the queue of prompt requests sent to Azure OpenAI during peak usage

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An automated system that suggests improvements to prompts based on output quality metrics

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A monitoring dashboard showing the flow of prompts through an AI application in production

Question 10

A customer service organization has thousands of support tickets labeled with predefined categories such as 'Billing', 'Technical', and 'Account Management'. They want to build a solution that automatically assigns a category to new, incoming tickets. The categories are fixed and known in advance. Which Azure AI Language service feature should they use?

Accepted Answer

Custom Text Classification. Custom Text Classification (B) is correct because the organization has a fixed set of predefined categories and needs to classify new support tickets into those categories. This feature allows you to train a custom model using labeled examples of 'Billing', 'Technical', and 'Account Management' tickets, enabling automatic assignment of incoming tickets to the correct category.

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Prebuilt Text Analytics

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Language Understanding (LUIS)

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Translator

Question 11

What is 'latent semantic analysis' (LSA) and how does it relate to modern NLP embeddings?

Accepted Answer

An early matrix factorisation method for finding latent semantic relationships — predecessor to neural embeddings. Latent Semantic Analysis (LSA) is an early matrix factorization technique that uses singular value decomposition (SVD) to reduce the dimensionality of a term-document matrix, revealing latent semantic relationships between words and documents. This approach is a direct predecessor to modern neural embeddings (e.g., Word2Vec, GloVe), which also learn low-dimensional vector representations of words based on co-occurrence patterns, but with deeper non-linear transformations.

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A legal analysis technique for discovering hidden clauses in contracts

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A technique for analysing the structure of sentences to detect grammatical errors

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Latent Semantic Analysis is the same as Large Language Model analysis

Question 12

What is data drift in the context of deployed machine learning models?

Accepted Answer

When production data distribution changes from the training data distribution over time. Data drift refers to the phenomenon where the statistical properties of the input data a deployed model receives in production change over time, diverging from the distribution of the data used during training. This degradation can cause the model's predictions to become less accurate or unreliable, even if the model itself remains unchanged. In Azure Machine Learning, data drift is monitored using dataset monitors that compare production data distributions against the training baseline.

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When training data is accidentally deleted from storage

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When a model's weights change during inference

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When data is moved between different Azure storage accounts

Question 13

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 14

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 15

What is 'wildlife monitoring' as a computer vision application and what Azure services power it?

Accepted Answer

Using computer vision to identify species, count populations, and track animals from camera trap images. 'wildlife monitoring' in the context of computer vision specifically refers to using AI to automatically analyze camera trap images to identify species, count populations, and track animal movements. Azure services such as Custom Vision (for training species-specific classifiers) and Computer Vision (for image analysis) power this by processing images captured in the field, enabling conservationists to gather data without manual review.

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CCTV monitoring of wildlife parks to ensure visitor safety from animal encounters

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Real-time video monitoring of endangered animal exhibits in zoos for welfare compliance

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AI-powered smart thermostats that monitor and adapt wildlife sanctuary temperatures