AI-900 Practice Test 26 — 15 Questions

Question 1

A city government is planning to deploy an AI system that analyzes security camera footage to detect potential crimes in real-time. Citizens express concerns about privacy and potential misuse. Which Microsoft responsible AI principle should the government prioritize to address these concerns?

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

Accepted Answer

Privacy and security. The citizens' concerns center on unauthorized access to sensitive video data and potential misuse of surveillance footage. Microsoft's Privacy and security principle directly addresses these issues by mandating data protection, transparency in data collection, and user control over personal information. Prioritizing this principle ensures the AI system complies with regulations like GDPR and implements encryption, access controls, and data minimization to safeguard citizen privacy.

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Fairness

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Reliability and safety

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Inclusiveness

Question 2

What is the purpose of Azure AI Language's 'entity linking' feature?

Accepted Answer

Identifying entities in text and connecting them to a knowledge base to disambiguate meaning. Entity linking in Azure AI Language identifies named entities in text and disambiguates them by linking to a corresponding entry in a knowledge base, such as Wikipedia or Microsoft's internal knowledge graph. This resolves cases where the same name could refer to multiple real-world entities (e.g., 'Washington' could be a state, a person, or a city), ensuring the correct meaning is assigned.

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Creating hyperlinks in documents to connect related sections

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Linking multiple Azure AI Language projects together

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Connecting extracted entities to a CRM database for business intelligence

Question 3

What is prompt engineering?

Accepted Answer

The practice of designing effective inputs to guide AI model outputs. Prompt engineering is the practice of designing and refining input prompts (text instructions) to guide the behavior and output of large language models (LLMs) like GPT-4 or Azure OpenAI. It leverages the model's pre-trained knowledge without modifying its weights, using techniques such as zero-shot, few-shot, or chain-of-thought prompting to achieve desired responses. This is a core skill in generative AI workloads because the quality of the output directly depends on the structure and specificity of the prompt.

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The process of training large language models from scratch

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A method of compressing AI models to run on smaller devices

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A way to fix bugs in AI software

Question 4

What is 'Azure AI Translator' and what are its main capabilities beyond basic text translation?

Accepted Answer

Document translation, custom terminology, transliteration, and dictionary lookup beyond basic text translation. Azure AI Translator is a cloud-based text translation service that goes far beyond basic text translation. Its main capabilities include document translation (preserving original structure), custom terminology (using user-defined glossaries), transliteration (converting text between scripts), and dictionary lookup (providing alternative translations and usage examples). These features make it suitable for enterprise-grade localization and multilingual content processing.

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Only translating short text snippets between two languages

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Interpreting idioms and cultural references to produce culturally appropriate translations

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Real-time video translation by overlaying translated subtitles on live video streams

Question 5

A law firm wants to automatically categorize incoming legal documents into custom categories such as 'Motion', 'Contract', 'Discovery', and 'Memorandum'. The firm has a set of manually labeled documents that can be used to train the system. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. The law firm needs to categorize documents into custom categories using their own labeled data. Custom text classification in Azure AI Language is specifically designed for this purpose, allowing you to train a model on your own labeled documents to classify text into user-defined categories. Prebuilt Text Analytics for sentiment only detects sentiment (positive/negative/neutral), not custom categories.

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Prebuilt Text Analytics for sentiment

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Conversational Language Understanding

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Key phrase extraction

Question 6

A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?

Accepted Answer

Azure Face API. The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.

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Azure Computer Vision - Image Analysis

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Azure Custom Vision

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Azure Form Recognizer

Question 7

What is 'form recognition' in Azure AI Document Intelligence and what types of forms does it support?

Accepted Answer

Extracting key-value pairs and tables from structured form documents using pre-built or custom models. Form recognition in Azure AI Document Intelligence (formerly Form Recognizer) is a specialized service that uses optical character recognition (OCR) and machine learning to extract key-value pairs, tables, and text from structured or semi-structured documents. It supports pre-built models for common forms like invoices and receipts, as well as custom models trained on user-provided form samples. Option B correctly describes this extraction capability.

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Recognising when a web form has been submitted by a user in a browser application

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Generating HTML forms automatically from a database schema

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Validating that completed forms meet schema and data type requirements

Question 8

What is 'training data' vs 'test data' in machine learning?

Accepted Answer

Training data fits the model; test data provides an unbiased estimate of real-world performance. Training data is used to fit the model's parameters (e.g., weights in a neural network or split criteria in a decision tree), while test data is held back and used only after training to evaluate the model's performance on unseen data. This separation provides an unbiased estimate of how the model will generalize to real-world data, which is critical for avoiding overfitting. In Azure Machine Learning, this split is typically managed via the `train_test_split` function or automated in AutoML pipelines.

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Training data is collected first; test data is older data from an archive

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Training data is labelled by humans; test data is labelled automatically by the model

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Test data is always larger than training data to ensure reliable evaluation

Question 9

What is 'Azure AI Immersive Reader' and what accessibility needs does it address?

Accepted Answer

An NLP-powered text accessibility tool with TTS, syllable highlighting, and translation for learners and readers with disabilities. Azure AI Immersive Reader is a natural language processing (NLP) tool designed to improve reading comprehension and accessibility. It uses text-to-speech (TTS), syllable highlighting, and real-time translation to support learners, individuals with dyslexia, and other reading disabilities, making option B correct.

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A VR headset that immerses users in a virtual reading environment

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A feature that reads 3D models and product descriptions aloud in e-commerce applications

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An AI that summarises long articles into concise bullet points for busy readers

Question 10

A manufacturing company uses an AI system to predict when machines will need maintenance. The system must work correctly under varying factory floor conditions such as temperature changes and noise levels. Which Microsoft responsible AI principle is most directly focused on ensuring the system performs reliably in these different conditions?

Accepted Answer

Reliability & Safety. B is correct because the Reliability & Safety principle ensures that AI systems operate consistently and predictably under varying conditions, such as temperature changes and noise levels on a factory floor. This principle mandates rigorous testing, monitoring, and fail-safe mechanisms to maintain performance and prevent harm when environmental factors deviate from expected ranges.

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Fairness

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Privacy & Security

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Inclusiveness

Question 11

A hospital uses an AI system to analyze patient records and provide treatment recommendations. They want to ensure that individual patients cannot be re-identified from the data used to train the model. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Privacy and security. The requirement to prevent re-identification of individual patients from training data directly aligns with the Privacy and Security principle. This principle mandates that data be anonymized or de-identified to protect personal information, ensuring that individuals cannot be traced back from the dataset. In AI systems, this involves techniques like differential privacy, which adds noise to data to obscure individual contributions while preserving overall statistical patterns.

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Fairness

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Inclusiveness

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Accountability

Question 12

What is Azure AI Language's 'custom summarization' capability?

Accepted Answer

Fine-tuning the summarization model on domain-specific documents for improved specialized summaries. Azure AI Language's custom summarization allows you to fine-tune a pre-trained summarization model using your own domain-specific documents. This enables the model to generate more accurate and relevant summaries for specialized fields like legal, medical, or financial texts, rather than relying solely on generic training data.

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Generating summaries with custom fonts and formatting styles

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Setting a custom character limit for all generated summaries

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Automating summary creation for all documents in an Azure storage account

Question 13

A manufacturing company wants to use Azure Computer Vision to inspect products on an assembly line for defects. They have a labeled dataset with images of defective and non-defective products. They need to not only classify products as defective or not, but also identify the exact location of the defect (e.g., a crack) in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.

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Custom Vision image classification

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Azure Face API

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Optical Character Recognition (OCR)

Question 14

A global company receives customer support tickets in over 60 languages. They need to automatically detect the language of each ticket so it can be routed to the appropriate language-specific team. The company has no labeled training data for language identification. Which Azure AI Language feature should they use?

Accepted Answer

Language Detection. Language Detection is the correct choice because it is a pre-built, zero-shot Azure AI Language feature that can automatically identify the language of text without requiring any labeled training data. The service uses a multilingual model trained on large datasets to detect over 100 languages, making it ideal for routing support tickets in over 60 languages with no prior customization.

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Custom Text Classification

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Key Phrase Extraction

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Entity Recognition

Question 15

What is the Azure AI Vision service's 'Image Analysis 4.0' major new capability compared to previous versions?

Accepted Answer

The Florence foundation model enabling detailed captions, dense captioning, background removal, and multimodal embeddings. Image Analysis 4.0 introduces the Florence foundation model, which significantly enhances image understanding capabilities. This model enables detailed captions, dense captioning (generating captions for multiple regions within an image), background removal, and multimodal embeddings that align images and text in a shared vector space. These features go far beyond the classification, object detection, and OCR capabilities of version 3.x.

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Support for processing video files, which was not available in version 3.x

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Support for the first time for color analysis features in images

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The ability to process images larger than 4MB for the first time