AI-900 Practice Test 25 — 15 Questions

Question 1

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?

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

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 2

A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves a low error on the training data but performs significantly worse when used to predict prices in a new city with different property characteristics. Which concept best explains this poor performance?

Accepted Answer

Overfitting. The model performs well on training data but poorly on new data from a different city, which is the classic symptom of overfitting. Overfitting occurs when a model learns noise and specific patterns in the training data that do not generalize to unseen data, especially when the new data has different characteristics (e.g., different property market dynamics). In this case, the model has memorized the training city's price patterns rather than learning generalizable relationships.

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Underfitting

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Data leakage

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Bias-variance tradeoff

Question 3

What is the purpose of 'top_p' (nucleus sampling) in Azure OpenAI API calls?

Accepted Answer

A sampling method that restricts token selection to the most probable token set. 'top_p' (nucleus sampling) in Azure OpenAI API calls controls the cumulative probability threshold for token selection. Instead of considering all possible next tokens, the model selects from the smallest set of tokens whose cumulative probability exceeds the 'top_p' value (e.g., 0.9 means the model considers only the top tokens that together have a 90% chance). This reduces randomness while allowing more natural variation than fixed 'top_k' sampling.

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The maximum number of paragraphs in the generated response

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A parameter that sets the minimum response quality threshold

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The priority level of the API request in a queue

Question 4

A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?

Accepted Answer

Custom Question Answering. Custom Question Answering (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 5

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. 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 6

A company wants to use Azure Computer Vision to automatically analyze images of handwritten forms and extract the text for data entry. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Azure Computer Vision's Optical Character Recognition (OCR) capability is specifically designed to extract printed or handwritten text from images, including forms. It uses the Read API, which is optimized for text-heavy documents and supports handwritten text recognition, making it the correct choice for this scenario.

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Image Analysis

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Face API

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Object Detection

Question 7

What is 'grounding with Bing search' in Microsoft Copilot?

Accepted Answer

Retrieving current web information from Bing to augment LLM responses beyond its training cutoff. Grounding with Bing search in Microsoft Copilot refers to the technique of retrieving real-time, current web information from Bing to augment the responses of a large language model (LLM) beyond its static training cutoff date. This allows Copilot to provide up-to-date answers on recent events, data, or topics not present in the model's original training corpus, effectively grounding the AI's output in verifiable, live web content.

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Using Bing Maps to provide location-based responses

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Translating Copilot responses using Microsoft's Bing Translator

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Using Bing advertising data to personalize AI responses

Question 8

What is the 'frequency penalty' parameter in Azure OpenAI API calls?

Accepted Answer

A parameter that reduces repetition of words already present in the response. The 'frequency penalty' parameter in Azure OpenAI API calls is designed to reduce the likelihood of the model repeating words or phrases that have already appeared in the generated response. It works by applying a penalty proportional to the frequency of tokens already used, encouraging more diverse and less repetitive text output. This is distinct from the 'presence penalty', which penalizes tokens based on whether they have appeared at all, regardless of frequency.

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A cost multiplier based on how often you call the API

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A rate limiting parameter controlling maximum API calls per minute

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A filter that removes profanity based on how frequently it appears

Question 9

A marketing team uses Azure OpenAI to generate social media posts. They want to ensure the generated text maintains a consistent, predictable brand voice without being overly creative or random. Which parameter should they primarily adjust to control the randomness of the output?

Accepted Answer

Temperature. Temperature controls the randomness of token selection by scaling the logits before applying the softmax function. A lower temperature (e.g., 0.2) makes the model more deterministic and conservative, producing outputs that stick closely to the most likely tokens—ideal for maintaining a consistent, predictable brand voice. Higher temperatures increase randomness, which the team wants to avoid.

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Max tokens

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Frequency penalty

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Top P

Question 10

A city government is planning to deploy an AI system that analyzes security camera footage to detect potential crimes in real-time. Citizens express concerns about privacy and potential misuse. Which Microsoft responsible AI principle should the government prioritize to address these concerns?

Accepted Answer

Privacy and security. The citizens' concerns center on unauthorized access to sensitive video data and potential misuse of surveillance footage. Microsoft's Privacy and security principle directly addresses these issues by mandating data protection, transparency in data collection, and user control over personal information. Prioritizing this principle ensures the AI system complies with regulations like GDPR and implements encryption, access controls, and data minimization to safeguard citizen privacy.

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Fairness

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Reliability and safety

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Inclusiveness

Question 11

What is the purpose of Azure AI Language's 'entity linking' feature?

Accepted Answer

Identifying entities in text and connecting them to a knowledge base to disambiguate meaning. Entity linking in Azure AI Language identifies named entities in text and disambiguates them by linking to a corresponding entry in a knowledge base, such as Wikipedia or Microsoft's internal knowledge graph. This resolves cases where the same name could refer to multiple real-world entities (e.g., 'Washington' could be a state, a person, or a city), ensuring the correct meaning is assigned.

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Creating hyperlinks in documents to connect related sections

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Linking multiple Azure AI Language projects together

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Connecting extracted entities to a CRM database for business intelligence

Question 12

What is prompt engineering?

Accepted Answer

The practice of designing effective inputs to guide AI model outputs. Prompt engineering is the practice of designing and refining input prompts (text instructions) to guide the behavior and output of large language models (LLMs) like GPT-4 or Azure OpenAI. It leverages the model's pre-trained knowledge without modifying its weights, using techniques such as zero-shot, few-shot, or chain-of-thought prompting to achieve desired responses. This is a core skill in generative AI workloads because the quality of the output directly depends on the structure and specificity of the prompt.

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The process of training large language models from scratch

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A method of compressing AI models to run on smaller devices

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A way to fix bugs in AI software

Question 13

What is 'Azure AI Translator' and what are its main capabilities beyond basic text translation?

Accepted Answer

Document translation, custom terminology, transliteration, and dictionary lookup beyond basic text translation. Azure AI Translator is a cloud-based text translation service that goes far beyond basic text translation. Its main capabilities include document translation (preserving original structure), custom terminology (using user-defined glossaries), transliteration (converting text between scripts), and dictionary lookup (providing alternative translations and usage examples). These features make it suitable for enterprise-grade localization and multilingual content processing.

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Only translating short text snippets between two languages

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Interpreting idioms and cultural references to produce culturally appropriate translations

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Real-time video translation by overlaying translated subtitles on live video streams

Question 14

A law firm wants to automatically categorize incoming legal documents into custom categories such as 'Motion', 'Contract', 'Discovery', and 'Memorandum'. The firm has a set of manually labeled documents that can be used to train the system. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. The law firm needs to categorize documents into custom categories using their own labeled data. Custom text classification in Azure AI Language is specifically designed for this purpose, allowing you to train a model on your own labeled documents to classify text into user-defined categories. Prebuilt Text Analytics for sentiment only detects sentiment (positive/negative/neutral), not custom categories.

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Prebuilt Text Analytics for sentiment

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Conversational Language Understanding

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Key phrase extraction

Question 15

A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?

Accepted Answer

Azure Face API. The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.

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Azure Computer Vision - Image Analysis

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Azure Custom Vision

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Azure Form Recognizer