AI-900 Practice Test 22 — 15 Questions

Question 1

What is 'image captioning' in Azure AI Vision and how is it different from image tagging?

Accepted Answer

Captioning generates a natural language sentence describing the scene; tagging returns individual concept keywords. Image captioning in Azure AI Vision uses a deep learning model to analyze the entire scene and generate a coherent, natural language sentence describing the image content, such as 'a group of people playing soccer in a park.' In contrast, image tagging returns a list of individual keywords or concepts (e.g., 'soccer,' 'grass,' 'people') without forming a complete sentence. This distinction is fundamental to understanding the different outputs of these two Azure AI Vision features.

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Captioning adds user-written descriptions; tagging uses AI to detect objects automatically

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Captioning works on video; tagging works only on still images

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Image tagging is more accurate than captioning because it uses simpler classification

Question 2

What does 'AI-powered search' mean and how does it differ from traditional keyword search?

Accepted Answer

Understanding query meaning and intent to return relevant results beyond exact keyword matching. AI-powered search uses natural language processing (NLP) and machine learning models to interpret the user's intent and the semantic meaning of a query, rather than relying solely on exact keyword matches. This allows the search engine to return relevant results even when the query uses synonyms, paraphrases, or natural language phrasing. In contrast, traditional keyword search only matches documents containing the exact words or phrases from the query, often missing context or user intent.

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Using AI to speed up the indexing of documents in a search engine

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Automatically correcting user spelling mistakes before processing search queries

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Personalising search results for each user based on their browsing history

Question 3

A marketing team needs to analyze thousands of product reviews to identify the most frequently mentioned topics, such as 'battery life', 'customer support', and 'price'. They want an automated method to extract these main concepts from each review. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the main concepts, such as 'battery life', 'customer support', and 'price', from unstructured text like product reviews. This feature is specifically designed to extract the most salient topics or points from a document, making it ideal for analyzing thousands of reviews to find frequently mentioned themes.

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Language detection

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Sentiment analysis

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Entity recognition

Question 4

A company plans to use an AI system to analyze employee email communications to identify patterns and improve productivity. The company is concerned about respecting employee boundaries and legal regulations. Which Microsoft responsible AI principle is most important to consider?

Accepted Answer

Privacy and security – protecting employees' personal data and email content.. The scenario involves analyzing employee email communications, which inherently includes sensitive personal data and private correspondence. Microsoft's 'Privacy and security' principle is the most relevant because it mandates that AI systems protect individuals' data and respect boundaries, ensuring compliance with regulations like GDPR and internal privacy policies. Without strong privacy and security safeguards, analyzing email content could violate employee trust and legal requirements, regardless of how fair, reliable, or inclusive the system is.

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Fairness – ensuring the system treats all employees equally.

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Reliability and safety – ensuring the system functions correctly.

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Inclusiveness – ensuring the system works for all employees regardless of communication style.

Question 5

A hospital is deploying an AI system that recommends treatment plans based on patient data. The chief medical officer insists that doctors must be able to understand why the AI recommended a specific treatment. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Transparency. Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable by humans. In this scenario, the chief medical officer's demand that doctors must understand why the AI recommended a specific treatment directly aligns with transparency, which includes providing explanations for model outputs, such as feature importance or decision paths, to enable clinical validation and trust.

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Reliability and safety

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Fairness

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Accountability

Question 6

A library wants to digitize a collection of old printed books by converting scanned pages into searchable, editable text. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For digitizing old printed books, OCR can process scanned pages to produce digital text that can be indexed and edited, directly meeting the library's requirement.

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Image Analysis (descriptions and tags)

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Object detection

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Face detection

Question 7

A medical research team wants to analyze MRI scans to identify and measure the precise boundaries of tumors. They need to assign each pixel in the image to a class (e.g., tumor, healthy tissue, background). Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation assigns a class label to every pixel in an image, making it the correct choice for precisely delineating tumor boundaries in MRI scans. Azure Computer Vision's semantic segmentation capability outputs a pixel-level mask, enabling the research team to differentiate tumor, healthy tissue, and background at the finest granularity.

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Object Detection

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Image Classification

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Optical Character Recognition

Question 8

What is the difference between Azure AI Vision and Azure AI Custom Vision in terms of when to use each?

Accepted Answer

Use Azure AI Vision for general image analysis; use Custom Vision when you need specialized domain-specific recognition. Azure AI Vision is a pre-trained service for general image analysis tasks like object detection, OCR, and description generation, requiring no custom training. Azure AI Custom Vision allows you to train a model on your own labeled images for specialized, domain-specific recognition tasks, such as identifying unique product defects or rare animal species. Option B correctly captures this distinction: use Azure AI Vision for broad, out-of-the-box capabilities and Custom Vision when you need tailored recognition for your specific use case.

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Use Azure AI Vision for large images; use Custom Vision for small images

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Use Azure AI Vision only in production; Custom Vision only in development

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Use Azure AI Vision for images from cameras; Custom Vision for images from documents

Question 9

A brand monitoring company wants to automatically detect the presence of specific logos (e.g., Apple, Coca-Cola) in social media images. The logos can appear in various orientations and sizes within the image. Which Azure Computer Vision capability is specifically designed to identify popular brands from their logos?

Accepted Answer

Brand Detection. Brand Detection is a specialized Azure Computer Vision capability that uses a pre-trained model to identify thousands of global brands from their logos in images. It is specifically designed to handle variations in logo orientation, size, and placement, making it the correct choice for this scenario.

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Image Classification

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Object Detection

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Optical Character Recognition

Question 10

What is 'agent orchestration' in multi-agent AI systems?

Accepted Answer

Coordinating multiple AI agents — planning tasks, delegating to specialists, and synthesising outputs. Agent orchestration in multi-agent AI systems refers to the coordination of multiple AI agents, where a central orchestrator plans tasks, delegates them to specialized agents, and synthesizes their outputs into a coherent result. This is a core pattern in complex AI workflows, enabling modularity and specialization, unlike simple load balancing or code management.

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Scheduling when AI agents run to balance compute load across Azure regions

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Training a single model that can perform multiple specialised tasks simultaneously

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Organising AI agent code in a Git repository for version control

Question 11

What is 'computer vision' and which tasks does it encompass?

Accepted Answer

The AI field enabling machines to interpret images and video — covering classification, detection, segmentation, and OCR. Computer vision is a subfield of AI that enables machines to derive meaningful information from digital images, videos, and other visual inputs. It encompasses a broad range of tasks including image classification (labeling an entire image), object detection (locating and classifying multiple objects), image segmentation (pixel-level partitioning), and optical character recognition (OCR) for text extraction. Option B correctly captures this full scope, making it the right answer.

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Computer vision only includes OCR and text extraction from documents

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Computer vision is exclusively used for medical imaging diagnosis

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The field of designing displays and cameras for computers

Question 12

What is 'AI governance' and what tools does Azure provide for it?

Accepted Answer

The policies, processes, and controls ensuring AI systems are developed and operated responsibly. AI governance refers to the framework of policies, processes, and controls that guide the responsible development, deployment, and operation of AI systems. Azure provides tools like Azure Policy, Azure Role-Based Access Control (RBAC), and Microsoft Purview to enforce governance rules, audit AI usage, and ensure compliance with ethical standards. Option B correctly captures this definition, as it focuses on the organizational and technical mechanisms for responsible AI, not external restrictions or certifications.

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Government regulation that prohibits certain types of AI systems

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Electing a board of AI experts to approve all AI projects before they go to production

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Restricting AI development to organisations with formal AI certifications

Question 13

What is the difference between narrow AI and general AI?

Accepted Answer

Narrow AI excels at one specific task; general AI would have human-like intelligence across all domains. Narrow AI (also called weak AI) is designed and trained to perform a single specific task, such as image recognition or language translation, while general AI (strong AI) would possess the ability to understand, learn, and apply intelligence across a wide range of tasks at a human-like level. General AI remains a theoretical concept and has not been achieved, whereas narrow AI powers virtually all current AI systems, including those on Azure like Computer Vision and Language Understanding (LUIS).

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Narrow AI is more powerful than general AI

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Narrow AI runs on-premises; general AI runs in the cloud

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Narrow AI is for businesses; general AI is for consumers

Question 14

What is the primary difference between GPT models and DALL-E models from OpenAI?

Accepted Answer

GPT generates text; DALL-E generates images from text descriptions. GPT (Generative Pre-trained Transformer) models are designed to generate human-like text based on input prompts, while DALL-E models are specifically trained to generate images from textual descriptions. Both are generative AI models from OpenAI, but they operate on different modalities: GPT processes and produces text, whereas DALL-E processes text and produces images.

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GPT processes audio; DALL-E processes video

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GPT is for classification; DALL-E is for regression

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GPT and DALL-E are the same model with different names

Question 15

What is 'brand detection' in Azure AI Vision?

Accepted Answer

Identifying well-known brand logos and their locations within images. Brand detection in Azure AI Vision is a specialized feature that uses computer vision models to identify well-known brand logos within images and return their locations as bounding box coordinates. It is part of the Image Analysis API, specifically under the 'brands' visual feature, and does not involve text analysis, resource tagging, or counterfeit detection.

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Detecting counterfeit products by analysing product images

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Analysing brand sentiment from customer review text

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Detecting when Azure resources have been tagged with incorrect brand naming conventions