AI-900 Practice Test 9 — 10 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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