AI-900 Practice Test 30 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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

Question 9

A developer is using Azure OpenAI Service to generate structured data in JSON format. They want to ensure that every response is valid JSON without adding instructions in every prompt. Which Azure OpenAI feature should they configure?

Accepted Answer

Set the response_format parameter to 'json_object'.. Azure OpenAI Service provides a `response_format` parameter that can be set to `json_object`, which instructs the model to always return valid JSON output. This ensures structured data without requiring the developer to include formatting instructions in every prompt, as the service enforces JSON schema compliance at the API level.

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Set the temperature parameter to a low value (e.g., 0).

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Set the top_p parameter to a high value (e.g., 1).

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Set the max_tokens parameter to a high value (e.g., 2000).

Question 10

What is 'Azure Machine Learning pipelines' and why are they used?

Accepted Answer

Reusable orchestrated workflows that automate and version-control the full ML training lifecycle. Azure Machine Learning pipelines are reusable orchestrated workflows that automate and version-control the full ML training lifecycle, including data preparation, training, evaluation, and deployment. They enable reproducibility, parallel execution of steps, and easy sharing across teams, which is why option B is correct.

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Network pipelines for transferring data between Azure regions at high speed

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CI/CD pipelines in Azure DevOps for deploying application code to production

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Data pipelines that ingest streaming data from IoT sensors into Azure storage

Question 11

What is 'text generation' as a generative AI capability and what are common use cases?

Accepted Answer

Creating new coherent text from prompts for writing, code, summaries, and conversational AI. Text generation in generative AI refers to the capability of models (like GPT-4 or GPT-3.5) to produce new, coherent text based on a given prompt. This includes tasks such as writing articles, generating code, creating summaries, and powering conversational AI agents. The key distinction is that the output is novel content, not a direct extraction or transformation of existing text.

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Extracting and copying text from scanned images using OCR

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Converting speech audio into a written transcript

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Formatting existing text by adding headings, bullets, and correct punctuation

Question 12

A legal firm needs to automatically sort incoming legal documents into predefined categories such as 'Contract', 'Brief', 'Motion', and 'Discovery'. They have a set of 500 manually labeled documents to use as examples. Which Azure AI Language feature should they use to build this classification system?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows the legal firm to train a model using their 500 labeled documents to classify text into predefined categories like 'Contract', 'Brief', 'Motion', and 'Discovery'. This feature enables supervised learning where the model learns from labeled examples to automatically sort incoming documents, which is exactly the requirement described.

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Key phrase extraction

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Named entity recognition

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Sentiment analysis

Question 13

A bank uses an AI system to approve or deny personal loan applications. Several customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide understandable reasons for the AI's decision?

Accepted Answer

Transparency. Transparency is the Microsoft responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the bank must provide clear, understandable reasons for loan denials, which directly aligns with transparency's goal of enabling users to understand how and why decisions are made. This principle ensures that AI outcomes are not opaque black-box decisions but can be explained in human terms.

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Reliability and safety

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Fairness

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Privacy and security

Question 14

What is feature engineering in machine learning?

Accepted Answer

Selecting, transforming, and creating input variables from raw data to improve model performance. Feature engineering is the process of selecting, transforming, and creating input variables (features) from raw data to improve the performance of machine learning models. This step is critical because the quality and relevance of features directly impact a model's ability to learn patterns and generalize to new data. In Azure Machine Learning, feature engineering is often performed using tools like the 'Feature Engineering' step in automated ML or custom Python scripts with libraries such as pandas and scikit-learn.

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Designing the hardware chips for running ML models

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Selecting which neural network layers to include in a model

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Writing code to deploy ML models as REST APIs

Question 15

A digital art library wants to automatically generate a list of relevant keywords (e.g., 'landscape', 'portrait', 'abstract', 'nature') for each image in their collection. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Tagging. Image Tagging (B) is the correct capability because it analyzes the content of an image and returns a set of relevant keywords (tags) based on the detected objects, scenes, and concepts. This directly matches the requirement to generate a list of keywords like 'landscape', 'portrait', 'abstract', and 'nature' for each image.

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Optical Character Recognition (OCR)

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Image Captioning

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Object Detection