AI-900 Practice Test 22 — 15 Questions

Question 1

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?

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

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.

Answer

Reliability and safety

Answer

Fairness

Answer

Accountability

Question 2

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.

Answer

Image Analysis (descriptions and tags)

Answer

Object detection

Answer

Face detection

Question 3

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.

Answer

Object Detection

Answer

Image Classification

Answer

Optical Character Recognition

Question 4

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.

Answer

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

Answer

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

Answer

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

Question 5

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.

Answer

Image Classification

Answer

Object Detection

Answer

Optical Character Recognition

Question 6

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.

Answer

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

Answer

Training a single model that can perform multiple specialised tasks simultaneously

Answer

Organising AI agent code in a Git repository for version control

Question 7

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.

Answer

Computer vision only includes OCR and text extraction from documents

Answer

Computer vision is exclusively used for medical imaging diagnosis

Answer

The field of designing displays and cameras for computers

Question 8

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.

Answer

Government regulation that prohibits certain types of AI systems

Answer

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

Answer

Restricting AI development to organisations with formal AI certifications

Question 9

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).

Answer

Narrow AI is more powerful than general AI

Answer

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

Answer

Narrow AI is for businesses; general AI is for consumers

Question 10

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.

Answer

GPT processes audio; DALL-E processes video

Answer

GPT is for classification; DALL-E is for regression

Answer

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

Question 11

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.

Answer

Detecting counterfeit products by analysing product images

Answer

Analysing brand sentiment from customer review text

Answer

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

Question 12

What is 'personally identifiable information' (PII) detection in Azure AI Language?

Accepted Answer

Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text. PII detection in Azure AI Language is a pre-built feature that identifies sensitive personal data such as names, social security numbers, email addresses, and phone numbers within unstructured text. It can also redact (mask) these entities to help comply with data privacy regulations like GDPR. This is a core capability of the Azure AI Language service's Text Analytics API.

Answer

Verifying that a user's identity matches their stated credentials during login

Answer

Tracking which users have accessed personally sensitive Azure resources

Answer

Detecting when users are sharing their own personal information in an inappropriate context

Question 13

What is 'quality control' computer vision and how is it used in manufacturing?

Accepted Answer

Detecting manufacturing defects at production line speeds with consistent accuracy. Quality control in computer vision refers to using AI models to inspect products on a manufacturing line, detecting defects such as scratches, dents, or misalignments at high speed. Azure Custom Vision or Azure Computer Vision can be trained on labeled images of good and defective items to perform real-time inference, ensuring consistent accuracy far beyond human visual inspection. This directly addresses the need for automated, scalable defect detection in production environments.

Answer

Monitoring the quality of AI model outputs to ensure they meet accuracy standards

Answer

Verifying that factory video surveillance cameras meet quality standards

Answer

Controlling the quality of training images used to build computer vision models

Question 14

A hospital has a dataset with historical patient records, each labeled as either 'readmitted within 30 days' or 'not readmitted'. The hospital wants to train a model to predict which current patients are likely to be readmitted. Which type of machine learning task is this?

Accepted Answer

Supervised classification. This is a supervised classification task because the dataset contains labeled historical patient records (readmitted or not readmitted), and the goal is to predict a discrete category (binary outcome) for new patients. In Azure Machine Learning, this would use a classification algorithm like logistic regression or decision tree to assign each patient to one of the two classes.

Answer

Supervised regression

Answer

Unsupervised clustering

Answer

Reinforcement learning

Question 15

What is 'Bayesian optimisation' in hyperparameter tuning?

Accepted Answer

A smart hyperparameter search that uses past trial results to select promising configurations. Bayesian optimisation is a smart hyperparameter search method that builds a probabilistic model (typically a Gaussian process) of the objective function based on past trial results. It uses an acquisition function (e.g., Expected Improvement) to balance exploration and exploitation, selecting the most promising hyperparameter configurations to evaluate next. This makes it far more efficient than grid or random search for expensive-to-evaluate models.

Answer

A statistical method for updating model confidence as new training data arrives

Answer

An automatic method for adjusting learning rate during training based on gradient information

Answer

A probabilistic approach to labelling uncertain training examples