AI-900 Practice Exam 64 — 20 Questions

Question 1

A developer uses Azure OpenAI to generate product descriptions. They provide five examples of product descriptions that follow a specific format (name, features, price, call to action). They then ask the model to write a new description for a given product, expecting the same format. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, which involves providing a small number of examples (in this case, five product descriptions) to guide the model's output format and style without updating the model's weights. This technique leverages the model's in-context learning ability to follow the demonstrated pattern for a new input.

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Fine-tuning

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Zero-shot learning

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Reinforcement learning

Question 2

What is the purpose of Azure Machine Learning's dataset versioning?

Accepted Answer

Tracking changes to training data over time to enable reproducibility and auditing. Azure Machine Learning's dataset versioning allows data scientists to track changes to training data over time by creating immutable snapshots of datasets. This ensures reproducibility of experiments and provides an audit trail, which is critical for compliance and debugging model performance regressions.

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Encrypting datasets with different security keys for each version

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Creating multiple copies of training data in different storage regions

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Limiting which team members can access different versions of training data

Question 3

A museum wants to create an application that automatically generates descriptive captions for uploaded photos of artworks. The captions should describe the main subject, scene, and artistic style. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (with description feature). Option B is correct because the Image Analysis capability in Azure Computer Vision includes a 'description' feature that generates human-readable captions summarizing the main subject, scene, and artistic style of an image. This is achieved through pre-trained deep learning models that analyze visual content and produce natural language descriptions, making it ideal for automatically captioning artwork photos.

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Optical Character Recognition (OCR)

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Face API

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Custom Vision (object detection)

Question 4

A retail company wants to use security cameras to analyze customer flow. They need to detect when a person enters a specific store zone, count how many people are in that zone at any given time, and track the direction each person moves within the zone. Which Azure Computer Vision capability should they use?

Accepted Answer

Spatial Analysis. Spatial Analysis is the correct Azure Computer Vision capability because it is specifically designed to analyze video feeds from cameras to detect people, count them in defined zones, and track their movement direction. Unlike general object detection, Spatial Analysis provides the specialized functions for zone occupancy and person trajectory tracking required by the retail scenario.

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Object detection

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 5

What is a bot in the context of Azure Bot Service?

Accepted Answer

A software program that engages in natural language conversations with users. In the context of Azure Bot Service, a bot is a software program that uses natural language processing (NLP) to engage in conversational interactions with users. It leverages the Bot Framework SDK and can be integrated with channels like Microsoft Teams, Slack, or web chat to handle dialogues, answer questions, or perform tasks through text or speech.

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An automated robot that performs physical tasks in manufacturing

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A malicious program that attacks websites automatically

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An automated script for data backup in Azure storage

Question 6

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.

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

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

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

Question 7

What does Azure AI Vision's 'optical character recognition' (OCR) feature do?

Accepted Answer

Extracts printed and handwritten text from images and documents. Azure AI Vision's OCR feature is designed to extract printed and handwritten text from images and documents, converting visual text into machine-readable data. This is correct because OCR uses deep learning models to detect and read text characters from various visual sources, enabling downstream processing like search or analysis.

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Converts text files into images for archival purposes

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Recognises optical fibre cables in data centre photographs

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Corrects spelling errors in text extracted from forms

Question 8

What is 'explainable AI' (XAI) and why is it required in regulated industries?

Accepted Answer

AI systems that can explain their decisions in understandable terms — required for regulatory compliance. Explainable AI (XAI) refers to AI systems that provide human-understandable justifications for their decisions, predictions, or recommendations. In regulated industries such as finance, healthcare, and insurance, regulations like GDPR's 'right to explanation' and the EU AI Act require that automated decisions be transparent and auditable, making XAI a compliance necessity.

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AI systems with publicly available source code that anyone can inspect

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AI models that are simple enough for non-experts to rebuild from scratch

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AI systems that automatically explain errors in user-submitted code

Question 9

What is 'data drift' and why is it a concern for deployed AI models?

Accepted Answer

The gradual change in data distribution over time that causes deployed model accuracy to degrade. Data drift refers to the gradual change in the statistical properties of the input data that a deployed AI model receives, compared to the data it was trained on. This shift in distribution causes the model's predictions to become less accurate over time because the model was optimized for the original data patterns. In Azure Machine Learning, data drift is monitored using dataset monitors that compare baseline and target datasets to detect significant changes, triggering retraining pipelines to maintain model performance.

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When training data files are accidentally moved to the wrong storage container

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The movement of data between Azure regions for latency optimisation

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Intentional modification of training data to improve model robustness

Question 10

What is 'question answering' in Azure AI Language and what are its two main types?

Accepted Answer

Custom QA (trained on your documents) and prebuilt QA (document provided at query time). Option B is correct because Azure AI Language's 'question answering' feature provides two distinct capabilities: Custom QA, where you train a model on your own documents (e.g., PDFs, FAQs) to answer questions from that knowledge base, and Prebuilt QA, which uses a document provided at query time to extract answers without prior training. This distinction is fundamental to how the service is deployed—either as a persistent, trained knowledge base or as an on-the-fly extraction from a user-supplied document.

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Multiple-choice question generation and open-ended answer scoring

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Structured QA for databases and unstructured QA for text documents

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Real-time QA for chatbots and batch QA for scheduled document processing

Question 11

Match each Azure AI service tier to its description.

Question 12

Match each Azure AI service to its pricing model.

Question 13

What is 'cross-lingual transfer learning' in multilingual NLP models?

Accepted Answer

Using shared multilingual representations so knowledge learned in one language transfers to others. Cross-lingual transfer learning leverages shared multilingual representations (e.g., from models like multilingual BERT or XLM-R) that encode multiple languages into a common semantic space. This allows knowledge learned from training data in one language (e.g., English) to improve performance on tasks in other languages without requiring labeled data for each target language. The model transfers understanding of syntax, semantics, and context across languages because it was pre-trained on a diverse corpus of many languages simultaneously.

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Automatically translating training data from English to other languages before fine-tuning

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Using the same model for both NLP and computer vision tasks

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Transferring a model trained in Azure to run on another cloud provider

Question 14

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 15

What is 'AI reliability and safety' in Microsoft's Responsible AI principles?

Accepted Answer

AI performing consistently and safely across diverse conditions, with fail-safes and human oversight. B is correct because 'AI reliability and safety' in Microsoft's Responsible AI principles focuses on ensuring AI systems perform consistently and safely across diverse conditions, with built-in fail-safes and human oversight. This principle addresses the need for AI to handle edge cases, adversarial inputs, and unexpected scenarios without causing harm, aligning with Microsoft's commitment to trustworthy AI.

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Ensuring Azure AI infrastructure has 99.9% uptime SLA guarantees

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Using safety-certified AI models that have passed ISO security standards

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AI that passes software quality assurance testing before being deployed

Question 16

A company wants to implement an AI solution that treats all users fairly regardless of their background. Which Microsoft responsible AI principle does this requirement primarily address?

Accepted Answer

Fairness. The requirement to treat all users fairly regardless of background directly aligns with the Fairness principle, which mandates that AI systems should allocate outcomes, opportunities, or resources equitably and avoid discrimination based on sensitive attributes such as race, gender, or age. In Azure AI, this is operationalized through tools like Fairlearn and the Responsible AI dashboard, which assess and mitigate bias in model predictions. The other principles address different concerns: Privacy focuses on data protection, Inclusiveness on accessibility for diverse abilities, and Transparency on explainability of decisions.

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Privacy

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Inclusiveness

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Transparency

Question 17

What is the difference between 'supervised' and 'unsupervised' learning?

Accepted Answer

Supervised learning trains on labelled data; unsupervised discovers patterns in unlabelled data. Option B is correct because supervised learning requires a labeled dataset where each training example has an input-output pair, allowing the model to learn a mapping from inputs to known outputs. In contrast, unsupervised learning works with unlabeled data, and the model must find inherent patterns, groupings, or structures (e.g., clustering or dimensionality reduction) without any predefined labels. This distinction is fundamental to choosing the right machine learning approach in Azure, such as using Azure Machine Learning for supervised tasks like regression/classification or Azure Cognitive Services for unsupervised clustering.

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Supervised learning requires a human to watch the training process; unsupervised runs automatically

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Supervised learning uses neural networks; unsupervised uses decision trees only

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Unsupervised learning always produces better results than supervised learning

Question 18

What is 'healthcare AI' and what capabilities does Azure provide for it?

Accepted Answer

AI for extracting medical entities, radiology insights, clinical trial matching, and patient analysis. Option B is correct because healthcare AI refers to AI solutions tailored for the healthcare industry, and Azure provides specific capabilities such as extracting medical entities (e.g., symptoms, medications) via Azure Health Bot and Text Analytics for Health, analyzing radiology images with Azure AI Vision, matching patients to clinical trials using Azure Cognitive Services, and performing patient analysis with Azure Machine Learning. These capabilities support clinical decision-making and operational efficiency without replacing doctors.

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AI that gives patients direct medical advice as a substitute for doctors

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A hospital management system for scheduling, billing, and patient record management

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AI that monitors patients' vitals in real time using IoT medical devices

Question 19

A company uses Azure OpenAI Service to power a chat-based support assistant. They have extensive knowledge base documents that contain the correct information. The company wants the assistant to answer questions solely based on the provided documents and avoid generating plausible-sounding but incorrect information. Which approach should they implement to minimize the risk of such fabrications?

Accepted Answer

Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt. Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's responses in actual, retrieved document excerpts provided as context in the prompt. This ensures the assistant answers based solely on the supplied knowledge base, directly minimizing the risk of hallucination (plausible-sounding but incorrect information) by constraining the model to the retrieved facts.

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Increase the temperature parameter to 1.0 to force more creative responses

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Fine-tune the model on the knowledge base documents using supervised learning

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Use prompt engineering with a system message that tells the model to never make up facts

Question 20

A hotel chain wants to analyze thousands of guest reviews to understand the overall tone of feedback (positive or negative) and to extract the most commonly mentioned features (e.g., 'room cleanliness', 'staff friendliness', 'breakfast'). Which two Azure AI Language features should they combine?

Accepted Answer

Sentiment analysis and key phrase extraction. The hotel chain needs to determine the overall tone (positive/negative) of guest reviews and extract commonly mentioned features. Sentiment analysis provides the overall tone by assigning a sentiment score to each review, while key phrase extraction identifies the most salient phrases such as 'room cleanliness' and 'staff friendliness'. Combining these two Azure AI Language features directly addresses both requirements.

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Entity recognition and language detection

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Text summarization and sentiment analysis