AI-900 Practice Test 29 — 15 Questions

Question 1

A hospital deploys an AI system to assist with diagnosing diseases from medical images. A doctor disagrees with the system's diagnosis and overrules it. The hospital wants to document this interaction for legal and audit purposes. Which Microsoft responsible AI principle is most directly relevant?

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

Accepted Answer

Accountability. The scenario involves documenting a human override of an AI system's diagnosis for legal and audit purposes, which directly relates to accountability. Accountability in responsible AI ensures that organizations can answer for their AI systems' decisions by maintaining clear records of interactions, including when humans overrule AI outputs. This principle requires traceability and governance mechanisms, such as audit trails, to assign responsibility for outcomes.

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Fairness

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Reliability and safety

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Transparency

Question 2

What is Optical Character Recognition (OCR) and which Azure AI service provides it?

Accepted Answer

Technology that extracts text from images; provided by Azure AI Vision. Optical Character Recognition (OCR) is the technology that extracts printed or handwritten text from images, such as scanned documents or photos, and converts it into machine-readable text. This capability is provided by the Azure AI Vision service, specifically through its Read API, which can process both printed and handwritten text from a variety of image formats.

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Speech recognition; provided by Azure AI Speech

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Language translation; provided by Azure AI Translator

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Handwriting analysis for personality assessment; provided by Azure AI Face

Question 3

What is a large language model (LLM)?

Accepted Answer

An AI model trained on large amounts of text data that can generate and understand language. A large language model (LLM) is a type of AI model trained on vast amounts of text data using deep learning techniques, typically based on transformer architectures. It learns patterns, grammar, context, and even reasoning from the data, enabling it to generate coherent and contextually relevant text, as well as understand and respond to natural language inputs. This makes option B correct because it captures both the training foundation (large amounts of text data) and the core capabilities (generation and understanding).

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A database that stores large volumes of text documents

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A programming library for processing natural language

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A cloud service for translating documents

Question 4

What does Azure AI Vision return when it detects that an image may contain adult content?

Accepted Answer

Boolean flags and confidence scores for adult, racy, and gory content categories. Azure AI Vision's content moderation feature analyzes images for adult, racy, and gory content. It returns Boolean flags (indicating whether content is detected) and confidence scores (ranging from 0 to 1) for each category, allowing applications to make policy-based decisions without deleting or altering the original image.

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The image is immediately deleted from Azure Storage

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A list of specific body parts detected in the image

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An age verification requirement for the requesting user

Question 5

What is the purpose of Azure AI Vision's 'color analysis' feature?

Accepted Answer

Identifying dominant colors, accent colors, and whether images are black and white. Azure AI Vision's color analysis feature is designed to extract color information from images, including the dominant foreground and background colors, accent colors, and whether the image is black-and-white. This helps in understanding the visual composition and mood of an image, which is useful for applications like branding, content moderation, and image categorization.

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Detecting color defects in manufactured products

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Converting images to grayscale for accessibility

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Measuring the color accuracy of display screens

Question 6

A company wants to build a chatbot that answers customer questions using a large language model. The company has an extensive internal knowledge base with accurate, up-to-date product information. To ensure the chatbot's answers are based on this reliable source rather than the model's internal knowledge, which technique should they use?

Accepted Answer

Grounding with retrieval-augmented generation. Grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.

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Fine-tuning the model on the knowledge base

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Zero-shot learning

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Prompt engineering with few-shot examples

Question 7

What is 'AI in agriculture' (precision agriculture) and what AI technologies are applied?

Accepted Answer

Crop yield prediction, pest detection, irrigation optimisation, and crop health monitoring using ML and vision. Precision agriculture leverages machine learning (ML) and computer vision to analyze data from sensors, drones, and satellites for tasks like predicting crop yields, detecting pests, optimizing irrigation, and monitoring crop health. These AI technologies enable data-driven decisions that improve efficiency and sustainability in farming.

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AI that writes farming blogs and social media content for agricultural businesses

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Automating all farming tasks with AI-powered robots that replace farm workers

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Using AI to trade agricultural commodity futures on financial markets

Question 8

What does it mean to 'export' a model from Azure AI Custom Vision?

Accepted Answer

Downloading the trained model as a file for offline inference on edge devices. Exporting a model from Azure AI Custom Vision means downloading the trained model as a file (e.g., TensorFlow, ONNX, CoreML, or Docker container) so it can be run locally on edge devices without requiring an internet connection to the cloud API. This enables offline inference, reduced latency, and data privacy for scenarios like manufacturing or retail.

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Sharing the model configuration with other Azure subscriptions

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Moving the model from Custom Vision to Azure Machine Learning

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Submitting the model for Microsoft certification review

Question 9

A legal department needs to automatically extract specific types of information from court documents, such as the names of plaintiffs and defendants, dates of hearings, and names of presiding judges. The department has a large set of unlabeled documents but does not have any manually tagged examples. Which Azure AI Language feature should they use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct choice because it is a pre-built Azure AI Language feature designed to automatically identify and extract specific categories of information—such as person names, dates, and organizations—from unstructured text without requiring any labeled training data. The legal department's need to extract plaintiffs, defendants, hearing dates, and judges aligns directly with NER's out-of-the-box capabilities for common entity types like Person, Date, and Organization.

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Custom text classification

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Key phrase extraction

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Translation

Question 10

A developer uses Azure OpenAI Service to generate multiple alternative product slogans. The developer wants to get exactly 5 different slogan options in a single API call, each being a separate piece of text. Which parameter should the developer set to control the number of completions returned?

Accepted Answer

n. The 'n' parameter in Azure OpenAI Service specifies the number of completions (candidate responses) to generate for each API call. Setting n=5 returns exactly five distinct slogan options as separate text strings, fulfilling the requirement of a single request producing multiple alternatives.

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temperature

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max_tokens

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stop

Question 11

What is 'regularisation' in machine learning and what problem does it solve?

Accepted Answer

Adding a penalty to the loss function to discourage overly complex models and reduce overfitting. Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty discourages the model from learning overly complex patterns, such as large or numerous coefficients, which helps the model generalise better to unseen data. In Azure Machine Learning, regularisation parameters like L1 (Lasso) or L2 (Ridge) can be configured in algorithms such as linear regression or neural networks to control model complexity.

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Standardising input features to the same scale before training

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Applying government regulations to ensure AI models comply with data privacy laws

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Converting irregular training data shapes into a uniform format for the algorithm

Question 12

What are 'guardrails' in the context of responsible generative AI deployment?

Accepted Answer

Controls and filters that prevent generative AI from producing harmful or inappropriate outputs. Guardrails in responsible generative AI deployment refer to the system-level controls and filters that prevent the model from generating harmful, offensive, or inappropriate content. These are implemented through content filtering, prompt injection detection, and safety classifiers that intercept outputs before they reach the user. In Azure AI Services, guardrails are enforced via the Content Safety service and configurable filters in Azure OpenAI Service.

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Physical barriers in AI data centers for safety

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Rate limiting controls to prevent API overuse

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Version control systems for managing model updates

Question 13

What is 'ID document recognition' in Azure AI Document Intelligence?

Accepted Answer

Extracting structured fields (name, DOB, document number) from passports and identity cards. ID document recognition in Azure AI Document Intelligence is a prebuilt model designed to extract structured fields such as name, date of birth, document number, and expiration date from passports, driver licenses, and identity cards. It uses optical character recognition (OCR) and trained machine learning models to parse the document layout and return key-value pairs, not to verify authenticity or perform identity matching.

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Verifying whether a provided ID document is genuine or a counterfeit

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Assigning employee ID numbers to workers in an HR management system

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Recognising which employees are present using their ID badge photos

Question 14

What is 'Whisper' in Azure OpenAI and what can it do?

Accepted Answer

A speech recognition model that transcribes audio files to text across 100+ languages. Whisper is a speech recognition model available in Azure OpenAI that transcribes audio files into text. It supports over 100 languages and is designed for high accuracy in diverse acoustic environments, making it ideal for tasks like meeting transcription, voice note conversion, and multilingual audio processing.

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A low-power mode for running Azure OpenAI at reduced compute cost

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A secure communication channel for transmitting sensitive data to Azure OpenAI

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A text-to-speech model that generates very quiet, whispered audio output

Question 15

What is 'Azure AI Services multi-service resource' and what is its advantage?

Accepted Answer

A single resource providing one API key for Vision, Language, Speech, and Translator with unified billing. An Azure AI Services multi-service resource provides a single endpoint and API key to access multiple Azure AI services (Vision, Language, Speech, Translator) under one resource, enabling unified billing and simplified management. This is distinct from single-service resources, which require separate keys and endpoints for each service, increasing administrative overhead.

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A resource that automatically selects the best AI model for each request based on the task

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A resource type that runs multiple AI workloads simultaneously on shared compute

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An enterprise licence for unlimited usage of all Azure AI services