AI-900 Practice Test 29 — 15 Questions

Question 1

A legal department needs to automatically extract specific types of information from court documents, such as the names of plaintiffs and defendants, dates of hearings, and names of presiding judges. The department has a large set of unlabeled documents but does not have any manually tagged examples. Which Azure AI Language feature should they use?

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

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct choice because it is a pre-built Azure AI Language feature designed to automatically identify and extract specific categories of information—such as person names, dates, and organizations—from unstructured text without requiring any labeled training data. The legal department's need to extract plaintiffs, defendants, hearing dates, and judges aligns directly with NER's out-of-the-box capabilities for common entity types like Person, Date, and Organization.

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Custom text classification

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Key phrase extraction

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Translation

Question 2

A developer uses Azure OpenAI Service to generate multiple alternative product slogans. The developer wants to get exactly 5 different slogan options in a single API call, each being a separate piece of text. Which parameter should the developer set to control the number of completions returned?

Accepted Answer

n. The 'n' parameter in Azure OpenAI Service specifies the number of completions (candidate responses) to generate for each API call. Setting n=5 returns exactly five distinct slogan options as separate text strings, fulfilling the requirement of a single request producing multiple alternatives.

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temperature

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max_tokens

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stop

Question 3

What is 'regularisation' in machine learning and what problem does it solve?

Accepted Answer

Adding a penalty to the loss function to discourage overly complex models and reduce overfitting. Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty discourages the model from learning overly complex patterns, such as large or numerous coefficients, which helps the model generalise better to unseen data. In Azure Machine Learning, regularisation parameters like L1 (Lasso) or L2 (Ridge) can be configured in algorithms such as linear regression or neural networks to control model complexity.

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Standardising input features to the same scale before training

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Applying government regulations to ensure AI models comply with data privacy laws

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Converting irregular training data shapes into a uniform format for the algorithm

Question 4

What are 'guardrails' in the context of responsible generative AI deployment?

Accepted Answer

Controls and filters that prevent generative AI from producing harmful or inappropriate outputs. Guardrails in responsible generative AI deployment refer to the system-level controls and filters that prevent the model from generating harmful, offensive, or inappropriate content. These are implemented through content filtering, prompt injection detection, and safety classifiers that intercept outputs before they reach the user. In Azure AI Services, guardrails are enforced via the Content Safety service and configurable filters in Azure OpenAI Service.

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Physical barriers in AI data centers for safety

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Rate limiting controls to prevent API overuse

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Version control systems for managing model updates

Question 5

What is 'ID document recognition' in Azure AI Document Intelligence?

Accepted Answer

Extracting structured fields (name, DOB, document number) from passports and identity cards. ID document recognition in Azure AI Document Intelligence is a prebuilt model designed to extract structured fields such as name, date of birth, document number, and expiration date from passports, driver licenses, and identity cards. It uses optical character recognition (OCR) and trained machine learning models to parse the document layout and return key-value pairs, not to verify authenticity or perform identity matching.

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Verifying whether a provided ID document is genuine or a counterfeit

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Assigning employee ID numbers to workers in an HR management system

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Recognising which employees are present using their ID badge photos

Question 6

What is 'Whisper' in Azure OpenAI and what can it do?

Accepted Answer

A speech recognition model that transcribes audio files to text across 100+ languages. Whisper is a speech recognition model available in Azure OpenAI that transcribes audio files into text. It supports over 100 languages and is designed for high accuracy in diverse acoustic environments, making it ideal for tasks like meeting transcription, voice note conversion, and multilingual audio processing.

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A low-power mode for running Azure OpenAI at reduced compute cost

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A secure communication channel for transmitting sensitive data to Azure OpenAI

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A text-to-speech model that generates very quiet, whispered audio output

Question 7

What is 'Azure AI Services multi-service resource' and what is its advantage?

Accepted Answer

A single resource providing one API key for Vision, Language, Speech, and Translator with unified billing. An Azure AI Services multi-service resource provides a single endpoint and API key to access multiple Azure AI services (Vision, Language, Speech, Translator) under one resource, enabling unified billing and simplified management. This is distinct from single-service resources, which require separate keys and endpoints for each service, increasing administrative overhead.

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A resource that automatically selects the best AI model for each request based on the task

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A resource type that runs multiple AI workloads simultaneously on shared compute

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An enterprise licence for unlimited usage of all Azure AI services

Question 8

A retail company collects thousands of customer reviews. They want to automatically extract frequently mentioned aspects (e.g., 'battery life', 'customer service', 'price') to understand common topics. Which Azure AI Language capability should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language capability because it is specifically designed to identify and extract the main talking points or topics from unstructured text, such as 'battery life', 'customer service', and 'price' from customer reviews. This directly matches the requirement to automatically extract frequently mentioned aspects without needing predefined categories.

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Sentiment analysis

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Named entity recognition

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Language detection

Question 9

What is hyperparameter tuning in machine learning?

Accepted Answer

Searching for the best training configuration settings (learning rate, layers, etc.) to optimize model performance. Hyperparameter tuning is the process of systematically searching for the best combination of hyperparameters—such as learning rate, number of layers, batch size, or regularization strength—that control the training process itself, rather than being learned from data. In Azure Machine Learning, this is often automated using tools like HyperDrive, which runs multiple child runs with different hyperparameter configurations to find the set that maximizes model performance on a validation set.

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Adjusting the training data labels to improve model accuracy

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Reducing the number of features used by the model

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Updating model weights based on new production data

Question 10

What is the F1 score in machine learning evaluation?

Accepted Answer

The harmonic mean of precision and recall that balances both metrics. The F1 score is defined as the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall). This metric provides a single score that balances both false positives and false negatives, making it especially useful when classes are imbalanced. In Azure Machine Learning, the F1 score is a standard evaluation metric for classification models, reported in automated ML runs and designer modules.

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The first evaluation metric calculated before training a model

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The proportion of predictions correct on the test set

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A measure of how fast the model produces predictions

Question 11

What is 'health and safety monitoring' using computer vision and what scenarios does it address?

Accepted Answer

Using computer vision to detect PPE compliance, hazards, restricted zone entry, and safety violations. Health and safety monitoring using computer vision involves analyzing video feeds or images to automatically detect compliance with personal protective equipment (PPE) requirements, identify workplace hazards, monitor restricted zone entries, and flag safety violations. This is a core computer vision workload on Azure, leveraging services like Azure Video Indexer or Custom Vision to process visual data in real time, enabling proactive safety enforcement without human intervention.

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An employee wellness programme that tracks steps and exercise using wearables

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AI-powered medical diagnosis from health data captured by wearable sensors

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Monitoring employee screen time and break patterns for ergonomic health compliance

Question 12

What is Microsoft 365 Copilot and how does it use generative AI?

Accepted Answer

LLM-powered AI assistance embedded in Word, Excel, PowerPoint, and Teams for productivity tasks. Microsoft 365 Copilot is an AI assistant that integrates large language models (LLMs) with Microsoft Graph data and Microsoft 365 apps. It uses generative AI to create, summarize, and analyze content directly within Word, Excel, PowerPoint, and Teams, enhancing productivity without replacing the existing Office interface.

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An AI assistant that replaces Microsoft Office with a conversational interface

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An automated backup system for Microsoft 365 documents

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A virtual employee that works independently in Microsoft Teams

Question 13

What is the 'AI Bill of Materials' (AI BOM) concept in responsible AI?

Accepted Answer

A transparency document listing all components (data, models, code) used in an AI system. The AI Bill of Materials (AI BOM) is a transparency document that lists all components—such as datasets, models, code, and dependencies—used in building an AI system. It is analogous to a software bill of materials (SBOM) and is a key practice in responsible AI to ensure traceability, reproducibility, and accountability. Option B correctly identifies this purpose.

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A financial document listing the costs of AI infrastructure components

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A checklist of billing items for Azure AI services

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A list of materials needed to build an AI chatbot interface

Question 14

A hospital uses an AI system to recommend treatment plans for patients. The system's decision process is complex and not easily understood by doctors. The hospital wants to ensure that doctors can trust and verify the system's recommendations. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. The scenario describes a complex AI decision process that doctors cannot easily understand, which directly relates to the need for interpretability and openness about how the system works. Transparency is the Microsoft responsible AI principle that focuses on making AI systems understandable and providing clear documentation, so users can verify and trust the outputs. By ensuring transparency, the hospital can enable doctors to audit the reasoning behind treatment recommendations, fostering trust and accountability.

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Reliability and safety

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Fairness

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Privacy and security

Question 15

What is the primary benefit of using Retrieval Augmented Generation (RAG) over relying solely on an LLM's trained knowledge?

Accepted Answer

RAG grounds LLM responses in current, specific information — reducing hallucination and knowledge cutoff issues. RAG enhances LLM outputs by retrieving relevant, up-to-date information from an external knowledge base (e.g., Azure Cognitive Search) and injecting it into the prompt context. This grounds the model's response in verifiable data, significantly reducing hallucinations and overcoming the knowledge cutoff limitation inherent in static training data.

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RAG makes LLMs faster by skipping the training process

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RAG reduces the cost of API calls by batching requests

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RAG allows LLMs to process images alongside text