AI-900 Practice Exam 52 — 20 Questions

Question 1

A hospital deploys a machine learning model to screen patients for a rare disease. Only 0.1% of patients actually have the disease. The model correctly identifies most positive cases but also flags many healthy patients as potentially having the disease. The hospital wants to minimize the number of healthy patients who are incorrectly told they might have the disease. Which metric should the model optimize?

Accepted Answer

Precision. Precision measures the proportion of positive identifications that are actually correct. In this scenario, the hospital wants to minimize false positives (healthy patients incorrectly told they might have the disease). Optimizing precision directly reduces false positives, which is the stated goal.

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Recall

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F1 score

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Accuracy

Question 2

What is an AI agent in the context of Azure AI and generative AI?

Accepted Answer

An autonomous system using an LLM to plan and execute multi-step tasks using tools. Option B is correct because an AI agent in Azure AI and generative AI contexts refers to an autonomous system that leverages a large language model (LLM) to reason, plan, and execute multi-step tasks by calling external tools or APIs. This aligns with the Azure AI Agent Service, which enables agents to orchestrate workflows, retrieve information, and perform actions without continuous human intervention, embodying the core concept of agentic AI.

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A human employee who manages AI model deployments

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A monitoring agent that checks AI model health automatically

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A software robot that scrapes websites for training data

Question 3

A hospital is developing an AI system to assist doctors in diagnosing diseases from medical images. The system's predictions can influence patient treatment. Which Microsoft responsible AI principle is most important to ensure the system's decisions are accurate and reliable?

Accepted Answer

Reliability and Safety. In a medical diagnosis system, accuracy and reliability are paramount because incorrect predictions can directly lead to patient harm. The Reliability and Safety principle ensures the AI system performs consistently under expected conditions, with appropriate fail-safes and validation, which is the core requirement for clinical decision support.

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Fairness

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Privacy and Security

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Inclusiveness

Question 4

What does Azure AI Vision's 'people detection' (spatial analysis) feature track?

Accepted Answer

Counting, tracking movement, and measuring occupancy of people in defined zones from video. Azure AI Vision's spatial analysis (people detection) tracks the movement of people in video feeds, counting individuals and measuring how long they stay in defined zones. It does not identify specific people, detect masks or safety equipment, or measure body dimensions. This feature is designed for occupancy monitoring and flow analysis in physical spaces.

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Identifying the names of specific people in video footage

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Detecting whether people are wearing masks or safety equipment

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Measuring individual people's heights and body dimensions

Question 5

A logistics company needs to automatically read shipping labels on packages. The labels contain printed text in various fonts and sizes, as well as handwritten addresses. Which Azure Computer Vision capability should they use to extract the text from the labels?

Accepted Answer

Optical Character Recognition (OCR) - Read API. The Read API (part of Azure Computer Vision's OCR capabilities) is specifically designed to extract printed and handwritten text from images, handling varied fonts, sizes, and styles. This makes it the correct choice for reading shipping labels that contain both printed text and handwritten addresses.

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Image Analysis

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Face API

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Custom Vision

Question 6

What is the difference between entities and intents in conversational language understanding?

Accepted Answer

Intents represent the user's goal; entities are the specific pieces of information within the utterance. In conversational language understanding (CLU), intents represent the user's overall goal or desired action (e.g., 'BookFlight'), while entities are specific data points extracted from the utterance that provide context for that intent (e.g., 'New York' as a destination). This distinction is fundamental to natural language processing (NLP) on Azure, where intents map to actions and entities provide the parameters needed to fulfill those actions.

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Intents are for text; entities are for speech recognition

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Intents are predefined answers; entities are user questions

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They are the same concept with different names for clarity

Question 7

What is the Azure AI Language 'orchestration workflow' feature used for?

Accepted Answer

Routing user utterances to the appropriate CLU or QnA component based on intent. The orchestration workflow feature in Azure AI Language is designed to connect multiple Conversational Language Understanding (CLU) and Question Answering (QnA) projects into a single endpoint. It uses a top-level orchestrator model to classify the user's intent and then routes the utterance to the appropriate child project (CLU or QnA) for further processing, enabling a unified conversational experience across different knowledge bases.

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Automating data processing pipelines in Azure Data Factory

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Scheduling NLP model training jobs at regular intervals

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Translating utterances between languages before processing

Question 8

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 9

What is Microsoft 365 Copilot and how does it use generative AI?

Accepted Answer

LLM-powered AI assistance embedded in Word, Excel, PowerPoint, and Teams for productivity tasks. Microsoft 365 Copilot is an AI assistant that integrates large language models (LLMs) with Microsoft Graph data and Microsoft 365 apps. It uses generative AI to create, summarize, and analyze content directly within Word, Excel, PowerPoint, and Teams, enhancing productivity without replacing the existing Office interface.

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An AI assistant that replaces Microsoft Office with a conversational interface

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An automated backup system for Microsoft 365 documents

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A virtual employee that works independently in Microsoft Teams

Question 10

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

Accepted Answer

A security attack where malicious inputs try to override AI system instructions or bypass guardrails. Prompt injection is a security attack where a malicious user crafts input that overrides the AI system's original instructions or bypasses its guardrails, causing the model to behave in unintended ways. This is a critical security concern because it can lead to data exfiltration, unauthorized actions, or the generation of harmful content, undermining the trust and safety of AI deployments.

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A technique for making AI prompts shorter and more efficient

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The process of adding examples to prompts to improve model performance

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A method of injecting training data into a model after deployment

Question 11

A legal firm needs to automatically sort incoming legal documents into predefined categories such as 'Contract', 'Brief', 'Motion', and 'Discovery'. They have a set of 500 manually labeled documents to use as examples. Which Azure AI Language feature should they use to build this classification system?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows the legal firm to train a model using their 500 labeled documents to classify text into predefined categories like 'Contract', 'Brief', 'Motion', and 'Discovery'. This feature enables supervised learning where the model learns from labeled examples to automatically sort incoming documents, which is exactly the requirement described.

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Key phrase extraction

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Named entity recognition

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Sentiment analysis

Question 12

A legal firm needs to process thousands of contracts to automatically identify important terms such as dates, monetary amounts, names of parties, and legal citations. Which built-in feature of the Azure AI Language service is best suited for this task?

Accepted Answer

C) Entity Recognition. Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.

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A) Sentiment Analysis

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B) Key Phrase Extraction

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D) Language Detection

Question 13

A legal firm needs to analyze thousands of contracts to extract key information such as party names, dates, and monetary amounts. They also need to identify if certain clauses (e.g., non-compete, termination rights) are present. Which combination of Azure AI Language features should they use?

Accepted Answer

Named Entity Recognition and Key Phrase Extraction. Named Entity Recognition (NER) extracts predefined entities such as person names, dates, and monetary amounts from text, which directly addresses the need to identify party names, dates, and monetary amounts in contracts. Key Phrase Extraction identifies the main points or topics in a document, making it suitable for detecting the presence of specific clauses like non-compete or termination rights by surfacing the key phrases that represent those clauses.

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Entity Linking and Language Detection

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Sentiment Analysis and Key Phrase Extraction

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Text Analytics for Health and Entity Recognition

Question 14

A hospital wants to automatically extract patient symptoms and medication names from clinical notes. They have a set of pre-defined categories for symptoms and medications, and they have manually labeled a few hundred sentences to indicate which text spans belong to each category. Which Azure AI Language feature should they use to build this custom entity extraction solution?

Accepted Answer

Custom entity extraction. Custom entity extraction (D) is the correct choice because the hospital needs to identify specific text spans (symptoms and medication names) based on their own pre-defined categories, using a small set of manually labeled sentences for training. This is exactly what Azure's custom named entity recognition (NER) feature does—it allows you to train a model to extract custom entities from unstructured text, tailored to your domain-specific labels.

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Pre-built entity recognition

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Key phrase extraction

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Custom text classification

Question 15

A hotel booking website wants to automatically analyze guest-submitted photos of hotel rooms to verify if they contain common amenities such as a bed, a desk, and a chair. They want to use a prebuilt Azure AI service without any custom training. Which feature should they use?

Accepted Answer

Object Detection. Object Detection (prebuilt) is the correct choice because it can identify and locate multiple specific objects (bed, desk, chair) within an image by drawing bounding boxes around them. This prebuilt Azure AI Vision feature requires no custom training and directly supports detecting common amenities in hotel room photos.

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Optical Character Recognition (OCR)

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Image Analysis (prebuilt)

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Handwriting OCR

Question 16

What is 'semantic kernel' in Microsoft's AI development ecosystem?

Accepted Answer

An open-source SDK for orchestrating LLMs with plugins, memory, and planning for AI applications. Semantic Kernel is an open-source SDK that enables developers to integrate large language models (LLMs) with their applications by providing abstractions for plugins, memory (vector storage), and planning (automatic orchestration of AI tasks). It is not a core algorithm, a database, or a kernel modification, but rather a lightweight orchestrator that works with Azure OpenAI and other LLM providers.

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The core algorithm that powers all Azure AI services internally

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A database for storing semantic embeddings in Azure

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A Linux kernel modification for optimized AI workloads

Question 17

A legal firm needs to automatically extract custom entities such as 'Party Name', 'Effective Date', and 'Governing Law' from contracts. They have a set of manually labeled contracts to use as training data. Which Azure AI Language feature should they use?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows the legal firm to train a model on their manually labeled contracts to extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law'. Unlike built-in NER, which only recognizes predefined entity types, Custom NER learns from the provided labeled data to identify custom categories tailored to the firm's needs.

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Key Phrase Extraction

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Entity Linking

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Built-in Named Entity Recognition

Question 18

A global customer support team receives feedback messages in multiple languages. They want to build an automated pipeline that first identifies the language of each message, then translates it to English, and finally analyzes the sentiment of the translated text. Which combination of Azure AI services should they use?

Accepted Answer

Azure AI Translator and Azure AI Language (Text Analytics). Option B is correct because the pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.

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Azure AI Translator and Azure Anomaly Detector

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Azure AI Speech and Azure AI Language (Text Analytics)

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Azure AI Vision and Azure AI Language (Text Analytics)

Question 19

A global news organization receives articles in multiple languages. They need to first identify the language of each article, then translate it into English. Which combination of prebuilt Azure AI services should they use?

Accepted Answer

Language Detection (Azure AI Language) and Translator (Azure AI Translator). Option A is correct because the scenario requires two distinct steps: first, identifying the language of each article, which is performed by the Language Detection API in Azure AI Language; second, translating the article into English, which is handled by the Azure AI Translator service. These two prebuilt services are designed to work together seamlessly for multilingual content processing.

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Key Phrase Extraction and Sentiment Analysis

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Entity Recognition and Translator

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Language Detection and Text Analytics for Health

Question 20

What is the Azure AI Evaluation SDK used for in generative AI development?

Accepted Answer

Systematically measuring quality (groundedness, relevance, coherence) and safety of generative AI responses. The Azure AI Evaluation SDK is specifically designed to systematically measure the quality and safety of generative AI responses. It evaluates key metrics such as groundedness (how well the response aligns with source data), relevance, and coherence, as well as safety aspects like content filtering and harm detection. This makes it essential for validating and improving generative AI applications before deployment.

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Evaluating the environmental impact of AI model training

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Evaluating Azure subscription costs for AI workloads

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A peer review system for human evaluation of AI responses