AI-900 Practice Exam 54 — 20 Questions

Question 1

A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?

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

Accepted Answer

Use Azure OpenAI On Your Data to connect to a product database so the model retrieves and references only the provided specifications.. Azure OpenAI On Your Data allows the developer to ground the model's responses in a specific data source, such as a product database. This ensures the model retrieves and references only the provided specifications, preventing the generation of external or invented information (hallucinations). By using this feature, the model's outputs are strictly based on the connected data, aligning with the requirement for factual accuracy.

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Increase the frequency penalty to discourage the model from repeating common phrases.

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Decrease the temperature to 0 so the model always picks the most likely next token, making it more predictable.

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Enable content filtering to block any outputs that contain harmful or biased language.

Question 2

What is 'Azure Machine Learning workspace' and what does it contain?

Accepted Answer

The top-level Azure resource that organises all ML artefacts including models, experiments, and compute for a project. An Azure Machine Learning workspace is the top-level Azure resource that serves as a centralized hub for all machine learning activities. It contains essential artifacts such as datasets, experiments, models, pipelines, compute targets (e.g., compute clusters, inference clusters), and endpoints, enabling end-to-end ML lifecycle management within a single project.

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A physical office space at Microsoft where ML engineers develop Azure AI services

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A virtual desktop environment pre-configured with ML tools for data scientists

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A shared document repository for storing ML project documentation and reports

Question 3

A hospital uses an AI system to analyze patient health records for research. The hospital must ensure that all patient data is stored securely and only authorized personnel can access it. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Privacy and security. The scenario explicitly focuses on secure storage and access control of patient data, which directly aligns with Microsoft's responsible AI principle of Privacy and security. This principle ensures that data is protected against unauthorized access and breaches, often implemented through encryption (e.g., AES-256 for data at rest, TLS 1.2+ for data in transit) and role-based access control (RBAC) in Azure services like Azure SQL Database or Azure Blob Storage.

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Fairness

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Transparency

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Inclusiveness

Question 4

A market research firm wants to analyze thousands of customer reviews to automatically identify frequently mentioned product aspects and the sentiment associated with each aspect. For example, they want to know that 'battery life' is mentioned often with negative sentiment, while 'design' is mentioned with positive sentiment. Which Azure AI Language capability is best suited for this task?

Accepted Answer

Aspect-based Sentiment Analysis (Opinion Mining). Aspect-based Sentiment Analysis (Opinion Mining) is the correct choice because it goes beyond general sentiment to identify specific aspects (e.g., 'battery life', 'design') mentioned in text and the sentiment associated with each aspect. This directly matches the requirement to automatically detect frequently mentioned product aspects and their corresponding sentiments from customer reviews.

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Key Phrase Extraction

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Sentiment Analysis

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Named Entity Recognition (NER)

Question 5

A legal firm needs to analyze thousands of contracts to extract key information such as party names, dates, and monetary amounts. They also need to identify if certain clauses (e.g., non-compete, termination rights) are present. Which combination of Azure AI Language features should they use?

Accepted Answer

Named Entity Recognition and Key Phrase Extraction. Named Entity Recognition (NER) extracts predefined entities such as person names, dates, and monetary amounts from text, which directly addresses the need to identify party names, dates, and monetary amounts in contracts. Key Phrase Extraction identifies the main points or topics in a document, making it suitable for detecting the presence of specific clauses like non-compete or termination rights by surfacing the key phrases that represent those clauses.

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Entity Linking and Language Detection

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Sentiment Analysis and Key Phrase Extraction

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Text Analytics for Health and Entity Recognition

Question 6

A hospital deploys an AI system that predicts patient readmission risk within 30 days of discharge. The model uses features such as age, medical history, and treatment plans. The hospital discovers that the model has a significantly higher false positive rate for patients of a certain ethnic group compared to others, even though the model's overall accuracy is similar across groups. This disparity was not intentional. Which Microsoft responsible AI principle is most directly compromised?

Accepted Answer

Fairness. The Fairness principle requires AI systems to treat all groups equitably and avoid discrimination. A higher false positive rate for one ethnic group, even if unintentional, represents an unfair disparity. While Inclusiveness relates to designing for all people, Fairness specifically addresses equitable outcomes and bias mitigation, so it is the most directly compromised principle in this case.

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Reliability and safety

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Privacy and security

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Accountability

Question 7

What is an ML pipeline in Azure Machine Learning?

Accepted Answer

A workflow of connected steps for automating the end-to-end ML process. An ML pipeline in Azure Machine Learning is a workflow of connected steps that automates the end-to-end machine learning process, including data preparation, training, evaluation, and deployment. This enables reproducibility, reusability, and orchestration of complex ML tasks without manual intervention.

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The networking infrastructure connecting Azure ML compute nodes

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A data streaming service for real-time model predictions

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A GitHub repository for storing ML model code

Question 8

What is fine-tuning in the context of large language models?

Accepted Answer

Training a pre-trained model further on domain-specific data to improve task performance. Fine-tuning takes a pre-trained large language model (LLM) and continues the training process on a smaller, domain-specific dataset. This adjusts the model's weights to specialize its outputs for particular tasks (e.g., legal document summarization or medical Q&A) without retraining from scratch. It is distinct from prompt engineering or retrieval-augmented generation because it permanently modifies the model parameters.

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Adjusting the model's response speed for production deployment

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Manually reviewing and correcting model outputs

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Compressing a large model into a smaller, faster version

Question 9

A logistics company needs to automatically read shipping labels on packages. The labels contain printed text in various fonts and sizes, as well as handwritten addresses. Which Azure Computer Vision capability should they use to extract the text from the labels?

Accepted Answer

Optical Character Recognition (OCR) - Read API. The Read API (part of Azure Computer Vision's OCR capabilities) is specifically designed to extract printed and handwritten text from images, handling varied fonts, sizes, and styles. This makes it the correct choice for reading shipping labels that contain both printed text and handwritten addresses.

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Image Analysis

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Face API

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Custom Vision

Question 10

A developer uses Azure OpenAI Service to generate product descriptions. They want to ensure that the model only considers the most likely tokens that together have a cumulative probability of 0.95, ignoring very low-probability tokens that could lead to nonsensical outputs. Which parameter should they configure?

Accepted Answer

Top_p. (Top_p) is correct because the developer wants to limit token selection to those with a cumulative probability of 0.95, which is exactly what the Top_p (nucleus sampling) parameter controls. By setting Top_p to 0.95, the model will only consider the smallest set of tokens whose combined probability mass reaches 0.95, effectively ignoring low-probability tokens that could produce nonsensical outputs.

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Temperature

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Frequency penalty

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Presence penalty

Question 11

A law firm needs to automatically process incoming legal documents. They have two specific requirements: (1) extract the names of all parties involved, the court name, and the filing date; (2) categorize each document as a 'complaint', 'motion', or 'subpoena'. Which two Azure AI Language features should they use? (Choose two.)

Accepted Answer

Custom text classification. Custom text classification (C) is correct because it allows the law firm to train a model to categorize legal documents into custom classes like 'complaint', 'motion', or 'subpoena' based on labeled examples. This feature is designed for domain-specific classification tasks where predefined categories are insufficient.

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Sentiment analysis

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Key phrase extraction

Question 12

What is 'abstractive summarization' vs. 'extractive summarization' in Azure AI Language, and which produces summaries in new words?

Accepted Answer

Abstractive generates new sentences; extractive selects existing sentences from the source. Abstractive summarization generates new sentences that rephrase the core meaning of the source text, similar to how a human would summarize. Extractive summarization, in contrast, selects and copies key sentences directly from the original document without rewording them. Option B correctly identifies that abstractive produces new sentences while extractive selects existing ones.

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Extractive produces new words; abstractive copies sentences

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They produce identical output through different computational paths

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Abstractive works only for legal documents; extractive for general text

Question 13

A customer support team wants to analyze chat transcripts to identify the most common issues customers are reporting. They need to automatically extract meaningful phrases like 'slow internet connection' and 'billing error' from the conversations. Which Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct Azure AI Language feature because it is specifically designed to automatically identify and extract the most important words and phrases from unstructured text, such as 'slow internet connection' and 'billing error' from chat transcripts. This allows the support team to surface common issues without manual review. Sentiment Analysis, NER, and Language Detection serve different purposes and do not extract meaningful multi-word phrases.

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Sentiment Analysis

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Named Entity Recognition (NER)

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Language Detection

Question 14

A data scientist evaluates a regression model that predicts house prices. On the test set, the Mean Absolute Error (MAE) is $8,000 and the Root Mean Squared Error (RMSE) is $25,000. What does the large difference between MAE and RMSE indicate about the model's errors?

Accepted Answer

The model has some predictions with very large errors. The large difference between MAE ($8,000) and RMSE ($25,000) indicates that the model has some predictions with very large errors. RMSE squares the errors before averaging, which heavily penalizes large deviations, so a significantly higher RMSE relative to MAE suggests the presence of outliers or extreme prediction errors in the test set.

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The model is overfitting the training data

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The model predictions are consistently biased high

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The model has high variance due to outliers in training data

Question 15

What is the Azure AI Face service's 'liveness detection' feature used for?

Accepted Answer

Determining whether a face is from a live person or a spoofing attempt (photo/video/mask). Azure AI Face's liveness detection is specifically designed to differentiate between a real, live human face and spoofing artifacts such as printed photos, video replays, or 3D masks. It analyzes subtle cues like micro-movements, texture, and depth to verify the presence of a living person, preventing unauthorized access in facial recognition systems.

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Detecting whether a person is alive based on their vital signs

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Counting how many people are in a live video stream

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Monitoring whether a person remains present during a video call

Question 16

A law firm receives hundreds of legal documents daily. They need to automatically extract key entities like names of parties, dates, jurisdictions, and also classify each document as 'contract', 'pleading', or 'memo'. Which combination of Azure AI Language features should they use?

Accepted Answer

Entity recognition and custom text classification. The law firm needs both entity extraction (to identify parties, dates, jurisdictions) and document classification (contract, pleading, memo). Azure AI Language's prebuilt entity recognition handles the entity extraction, while custom text classification allows the firm to train a model to classify documents into their specific categories. This combination directly addresses both requirements without unnecessary features.

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Entity recognition and key phrase extraction

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Sentiment analysis and language detection

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Summarization and conversation analysis

Question 17

A manufacturing company wants to use computer vision to inspect products on an assembly line. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct choice because it not only classifies defects (e.g., scratch, dent, crack) but also provides bounding box coordinates to locate each defect within the product image. This meets the requirement to both identify and locate specific defect types on the assembly line.

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Image Classification

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Optical Character Recognition (OCR)

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Face Detection

Question 18

What is overfitting in machine learning?

Accepted Answer

When a model performs well on training data but poorly on new, unseen data. Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in high accuracy on training data but poor generalization to new, unseen data. This is a fundamental concept in ML because the goal is to create models that perform well on real-world data, not just the data they were trained on. In Azure Machine Learning, techniques like regularization, cross-validation, and early stopping are used to detect and mitigate overfitting.

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When a model is trained with too little data

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When a model takes too long to train

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When a model performs poorly on both training and test data

Question 19

A hospital deploys an AI system to predict patient readmission risk using historical health records. To protect patient privacy, the hospital wants to ensure that individual patients cannot be identified from the data used for training. Which responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Privacy and security. The requirement to prevent individual patient identification from training data directly aligns with the privacy and security principle, which mandates data anonymization, de-identification, and access controls. In AI systems, this is implemented through techniques like differential privacy (adding noise to data) or k-anonymity to ensure that outputs cannot be re-identified. The hospital's goal is to protect patient confidentiality, which is the core focus of this principle.

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Fairness

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Reliability and safety

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Inclusiveness

Question 20

A marketing team wants to automatically analyze thousands of customer reviews to identify the most commonly discussed aspects, such as 'price', 'durability', or 'customer service'. They do not have any labeled data for custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.

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Sentiment analysis

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Entity recognition

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Language detection