AI-900 Practice Test 4 — 25 Questions

Question 1

What is the purpose of Azure AI Vision's 'thumbnail generation' feature?

Accepted Answer

Generating crop-focused preview images that highlight the most important content area. Azure AI Vision's thumbnail generation feature analyzes the image content to identify the most important region (e.g., a person's face or a prominent object) and then crops the image around that region to produce a focused preview. This is distinct from simple resizing or compression, as it uses AI-based spatial analysis to preserve the key subject while discarding irrelevant background areas.

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Reducing file sizes of images for faster web page loading

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Creating thumbnail-sized AI model icons for the Azure portal

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Generating multiple image variations in different artistic styles

Question 2

What is 'Azure OpenAI's content filter' configurability and why does it matter?

Accepted Answer

Adjustable severity thresholds per harm category for legitimate domain-specific use cases. Azure OpenAI's content filter configurability allows administrators to adjust severity thresholds for each harm category (e.g., hate, violence, self-harm) to accommodate legitimate domain-specific use cases, such as medical or legal content that may require higher tolerance. This matters because it balances safety with utility, enabling organizations to fine-tune filtering based on their unique content policies and compliance needs without blocking valid applications.

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Configuring which users can access Azure OpenAI based on their location

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Setting the maximum token count before content is filtered for length

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Configuring which Azure OpenAI models are available to different teams within an organisation

Question 3

Which Azure AI service allows you to analyze medical images for clinical decision support?

Accepted Answer

Azure AI Health Insights and specialized medical imaging AI. Azure AI Health Insights includes specialized medical imaging AI capabilities designed to analyze radiology and other medical images for clinical decision support. This service is built on domain-specific models trained on medical data, unlike general-purpose computer vision services.

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Azure AI Custom Vision with medical training data

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Azure AI Translator for medical documents

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Azure AI Speech for dictation only

Question 4

What is the purpose of key phrase extraction in Azure AI Language?

Accepted Answer

To identify the main talking points and important concepts in text. Key phrase extraction in Azure AI Language is designed to identify the main talking points and important concepts within a given text. It analyzes the text structure and returns a list of key phrases that summarize the core topics, enabling quick understanding of the document's primary themes without reading the entire content.

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To translate key phrases between languages

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To classify text into positive or negative sentiment

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To generate new text based on key topics

Question 5

What is 'personalisation' as an AI workload and how does it differ from recommendation?

Accepted Answer

Dynamically adapting the full user experience for each individual based on their real-time behaviour. Personalisation as an AI workload involves dynamically adapting the full user experience—such as content, layout, or interactions—for each individual based on their real-time behaviour and historical data. This goes beyond simple recommendation by modifying the entire interface and flow, not just suggesting items. It leverages machine learning models that continuously learn from user actions to tailor the experience.

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Allowing users to customise the visual theme and layout of an application manually

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Recommending specific items a user might purchase based on their purchase history

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Creating personalised data privacy policies for each user based on their location

Question 6

A building management company develops an AI system that uses temperature and humidity sensors to automatically adjust the HVAC system. They want to ensure that the system does not inadvertently cause uncomfortable temperature swings for occupants. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Reliability and safety. The requirement to avoid uncomfortable temperature swings directly relates to the system's ability to operate reliably and safely under expected conditions. Microsoft's Reliability and safety principle ensures that AI systems perform consistently, fail gracefully, and do not cause physical harm or discomfort to users. In this HVAC scenario, the AI must be robust to sensor noise and environmental changes to maintain stable temperature control.

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Fairness

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Transparency

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Privacy and security

Question 7

A hospital collects patient feedback forms in text format. They want to automatically identify whether each feedback is positive, negative, or neutral, and also extract specific recurring phrases like 'waiting time' and 'staff attitude'. Which Azure AI Language feature should they use to determine the overall tone of the feedback?

Accepted Answer

C) Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to determine the overall tone (positive, negative, or neutral) of text. The question asks for identifying the tone of feedback, which is exactly what sentiment analysis provides by scoring each document and its sentences for sentiment polarity.

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A) Key phrase extraction

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B) Entity recognition

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D) Language detection

Question 8

What is 'AI democratisation' and how do Azure AI services support it?

Accepted Answer

Making AI capabilities accessible to all organisations and developers through pre-built APIs and low-code tools. AI democratisation refers to making AI capabilities accessible to a broad range of users, not just experts. Azure AI services support this by offering pre-built APIs (e.g., Computer Vision, Language Understanding) and low-code tools like Azure Machine Learning designer and Power Platform AI Builder, enabling developers and organisations with limited AI expertise to integrate AI into their applications without building models from scratch.

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Making AI governance decisions through a democratic voting process within organisations

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Ensuring AI companies are publicly listed so retail investors can participate in AI growth

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Open-sourcing all AI models so any developer can use them without licensing fees

Question 9

A quality inspection system uses cameras to examine metal parts for surface defects. The system must identify the exact location and shape of each scratch, dent, or crack. Which Azure Computer Vision capability is best suited for this?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image, allowing the system to precisely delineate the exact location, shape, and boundaries of surface defects like scratches, dents, or cracks on metal parts. This pixel-level granularity is essential for quality inspection where the geometry of each defect must be measured and analyzed.

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Image Classification

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Object Detection

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Dense Captioning

Question 10

What is 'AI enrichment' in the context of Azure AI Search (Cognitive Search)?

Accepted Answer

Applying AI cognitive skills during search indexing to extract and enrich content with metadata. AI enrichment in Azure AI Search refers to the process of applying built-in or custom cognitive skills during the indexing pipeline to extract, transform, and enrich unstructured data (e.g., images, text, PDFs) with additional metadata. This enables capabilities such as OCR, entity recognition, key phrase extraction, and language detection, turning raw content into searchable, structured information without requiring separate ML training.

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Adding premium features to an Azure AI subscription

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Training custom ML models to improve search result ranking

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Encrypting indexed search content with AI-managed keys

Question 11

What is 'multi-modal AI' and how does Azure AI Vision support it?

Accepted Answer

AI that processes and relates multiple data types (text, images, audio) together. Multi-modal AI refers to systems that can process and relate multiple types of data—such as text, images, and audio—simultaneously. Azure AI Vision supports this by providing pre-built models and APIs that extract information from images and video, which can then be combined with text or audio data in a multi-modal pipeline, enabling richer analysis like image captioning or visual question answering.

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AI that processes data in multiple programming languages simultaneously

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Deploying AI models across multiple Azure regions for global availability

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Using multiple AI models in sequence where each model processes a different step

Question 12

A social media platform wants to automatically review user-uploaded images to flag any that contain explicit or suggestive adult content, as well as violent imagery. Which Azure Computer Vision feature should they use?

Accepted Answer

Image Analysis - Moderate content. The 'Moderate content' feature of Azure Computer Vision is specifically designed to detect adult, suggestive, and violent content in images. It returns a binary flag and confidence scores for categories like adult, racy, and gory, making it the appropriate choice for automatically flagging explicit or violent user-uploaded images.

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Optical Character Recognition (OCR)

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Image Analysis - Tags

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Face Detection

Question 13

What is an endpoint in Azure Machine Learning?

Accepted Answer

A deployed ML model accessible via REST API for making predictions. In Azure Machine Learning, an endpoint is a REST API endpoint that exposes a deployed machine learning model for real-time inference. When you deploy a model to an Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) cluster, Azure ML creates a scoring URI that clients can call with HTTP POST requests containing input data, and the endpoint returns predictions. This enables applications to integrate model predictions via standard HTTPS protocol.

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A visual dashboard for monitoring model performance

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The final training step that produces a saved model file

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A data storage location for training datasets

Question 14

A company develops an AI system to recommend personalized news articles to users. The system uses collaborative filtering, suggesting articles that similar users have read. Which type of machine learning does this approach primarily rely on?

Accepted Answer

Unsupervised learning. Collaborative filtering identifies patterns in user-item interactions without labeled outcomes, grouping users or items based on similarity. This is a classic unsupervised learning task because the system discovers hidden structures (e.g., user clusters) from unlabeled data, rather than being trained on explicit input-output pairs.

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Supervised learning

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Reinforcement learning

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Semi-supervised learning

Question 15

What is 'extractive vs abstractive summarisation' and which does Azure AI Language's document summarisation feature support?

Accepted Answer

Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation. Azure AI Language's document summarization feature supports both extractive summarization (selecting key sentences from the original text) and abstractive summarization (generating a new, condensed summary that rephrases the content). Option B is correct because the service provides both capabilities, allowing users to choose based on their needs.

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Azure AI Language only supports extractive summarisation — abstractive requires Azure OpenAI

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Azure AI Language only supports abstractive summarisation because it is more advanced

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Extractive is for short texts; abstractive is required for documents longer than 10,000 words

Question 16

What is AutoML in Azure Machine Learning and what does it automate?

Accepted Answer

Automatically selecting algorithms, engineering features, and tuning hyperparameters to find the best model. AutoML in Azure Machine Learning automates the iterative process of algorithm selection, feature engineering, and hyperparameter tuning to identify the best-performing model for a given dataset. It systematically evaluates multiple machine learning pipelines and returns the model with the highest metric score, reducing manual trial-and-error. This helps data scientists and non-experts build high-quality models efficiently.

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Automatically deploying models to production without human review

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Automatically collecting and labeling training data from the internet

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Automatically writing Python code for custom ML algorithms

Question 17

What is the 'mean absolute error' (MAE) metric used to evaluate in machine learning?

Accepted Answer

The average absolute difference between regression model predictions and actual values. Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It measures how close predictions are to the true outcomes, with lower values indicating better model accuracy. In Azure Machine Learning, MAE is commonly used to evaluate regression models like linear regression or decision forests.

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The average confidence percentage of classification predictions

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The proportion of model predictions that deviate from expected values by more than a threshold

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How much the model's predictions differ from random chance

Question 18

A meeting transcription service needs to convert multilingual audio recordings into accurate text in real time. Which Azure OpenAI Service model is specifically designed for this task?

Accepted Answer

Whisper. Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.

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GPT-4

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DALL-E 2

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Codex

Question 19

What is 'Azure AI Foundry's model hub' and what models are available there?

Accepted Answer

A curated collection of leading AI models from OpenAI, Microsoft (Phi), Meta, Mistral, and others. Azure AI Foundry's model hub is a curated collection of leading AI models from providers like OpenAI, Microsoft (Phi), Meta, Mistral, and others. It enables developers to discover, compare, and deploy pre-built models for generative AI workloads without needing to train models from scratch. This aligns with the exam's focus on leveraging existing AI services in Azure.

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A marketplace where organisations can sell their custom-trained AI models to other Azure customers

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A version control system for AI models similar to Git for code

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A centralised repository of Microsoft's internal research models not available to customers

Question 20

A medical research organization uses an AI system to analyze patient health records to identify patterns in disease progression. They publish a research paper that includes tables of aggregated statistics derived from the data. Later, a researcher discovers that by combining multiple statistics, it is possible to identify individual patients. Which Microsoft responsible AI principle has been most directly compromised?

Accepted Answer

Privacy and security. The scenario describes a re-identification attack, where aggregated statistics (tables) can be combined to infer individual patient identities. This directly violates the privacy and security principle, which requires that AI systems protect personal data and prevent unauthorized identification. Microsoft's responsible AI principle of privacy and security emphasizes safeguarding data through techniques like differential privacy, which was not applied here.

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Fairness

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Transparency

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Accountability