AI-900 Practice Exam 64 — 20 Questions

Question 1

A retail company uses ceiling-mounted cameras to monitor shelf stock. They want an automated system that analyzes each camera image to detect if any product is missing from its expected location on the shelf (a product gap). Which Azure Computer Vision capability should they use?

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

Accepted Answer

Object detection. Object detection is the correct choice because it can identify and locate multiple objects (e.g., product boxes) within an image and determine if expected items are missing from their designated positions on the shelf. Unlike image classification, which assigns a single label to the entire image, object detection provides bounding boxes and class labels for each detected object, enabling precise gap analysis.

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Image classification

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Optical Character Recognition (OCR)

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Face detection

Question 2

What is 'fine-tuning' a language model and when should you use it instead of prompt engineering?

Accepted Answer

Further training a model on domain-specific data to change its behaviour permanently for a task. Fine-tuning is the process of taking a pre-trained language model and further training it on a domain-specific dataset to adapt its behavior permanently for a particular task. This is used instead of prompt engineering when the task requires consistent, specialized outputs that cannot be reliably achieved through prompt instructions alone, such as classifying medical records or generating legal documents.

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Fine-tuning repairs errors in a model's base training data

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Adjusting the model's temperature setting to produce more consistent outputs

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Selecting which pre-trained model from the Azure model catalogue best suits your task

Question 3

Which Azure AI service can analyze an image and return a description of its contents in natural language?

Accepted Answer

Azure AI Vision (Computer Vision). Azure AI Vision (Computer Vision) includes an image analysis API that can generate a human-readable description of an image's contents. This feature uses deep learning models to identify objects, actions, and scenes, then produces a natural language caption describing the image. The correct answer is B because this is the specific service designed for image understanding and description generation.

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Azure AI Language

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Azure AI Speech

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Azure Bot Service

Question 4

Which type of machine learning uses labeled training data where the correct output is provided for each input?

Accepted Answer

Supervised learning. Supervised learning is the correct answer because it explicitly uses labeled training data where each input example is paired with the correct output label. The algorithm learns to map inputs to outputs by minimizing the error between its predictions and the provided labels, enabling tasks like classification and regression.

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Unsupervised learning

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Reinforcement learning

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Transfer learning

Question 5

What is Azure AI Studio?

Accepted Answer

A unified platform for building, evaluating, and deploying generative AI applications. Azure AI Studio is a unified platform designed specifically for building, evaluating, and deploying generative AI applications. It integrates tools for prompt engineering, model fine-tuning, and safety evaluation, enabling developers to create custom AI solutions using large language models (LLMs) from Azure OpenAI Service and other sources. This makes option B correct as it directly describes the platform's core purpose.

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A video editing tool powered by AI for content creators

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A specialized IDE for writing Python machine learning code only

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A database service for storing conversation history from AI applications

Question 6

What is conversational language understanding (CLU) in Azure AI Language?

Accepted Answer

A feature that trains models to understand user intent and extract entities from natural language. Conversational Language Understanding (CLU) is a feature within Azure AI Language that enables you to build custom models for extracting intents (what the user wants to do) and entities (key pieces of information) from natural language utterances. Unlike pre-built or translation services, CLU is specifically designed for training and deploying a natural language understanding model tailored to your application's domain.

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A service that translates chatbot responses into multiple languages

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A service that converts speech to text for voice assistants

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A pre-built AI for answering FAQ questions automatically

Question 7

What is the purpose of Azure AI Language Studio?

Accepted Answer

A web-based UI for exploring and building NLP solutions without code. Azure AI Language Studio is a web-based user interface that allows users to explore, build, and manage natural language processing (NLP) solutions without writing code. It provides pre-built and customizable features such as sentiment analysis, key phrase extraction, language detection, and conversational language understanding, enabling rapid prototyping and integration of NLP capabilities into applications.

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A tool for training custom computer vision models

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A platform for managing Azure AI service billing

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A code editor for writing Python ML scripts

Question 8

What is the 'accountability' principle in Microsoft's responsible AI framework?

Accepted Answer

Humans should maintain responsibility and oversight over AI systems and their impacts. The 'accountability' principle in Microsoft's responsible AI framework means that humans are ultimately responsible for the design, deployment, and outcomes of AI systems. This principle ensures that AI systems are not autonomous decision-makers without human oversight; instead, organizations must maintain clear ownership and governance to address any unintended impacts or biases.

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AI systems should automatically fix their own errors

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AI systems should log all user interactions for auditing

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All AI code should be open-source for public review

Question 9

What is custom text classification in Azure AI Language?

Accepted Answer

Training a text classification model on your own labeled data for custom categories. Custom text classification in Azure AI Language allows you to train a machine learning model on your own labeled dataset to classify text into custom categories that are specific to your business needs. This is a supervised learning approach where you provide examples of text and their corresponding labels, and the service learns to predict the correct category for new, unseen text. It is distinct from pre-built classification models because it adapts to your unique taxonomy.

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Translating text into a custom language invented by the user

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Automatically detecting and removing custom offensive terms from text

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Formatting text with custom styles and fonts

Question 10

What does Azure AI Language's 'text analytics for health' feature provide?

Accepted Answer

Extracting structured clinical information from unstructured medical text. Azure AI Language's 'text analytics for health' feature is designed to extract structured clinical information—such as diagnoses, medications, symptoms, and procedures—from unstructured medical text like clinical notes, discharge summaries, and radiology reports. It uses specialized NLP models trained on medical ontologies (e.g., UMLS, ICD-10-CM) to identify and normalize healthcare entities, enabling downstream analytics and decision support.

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Real-time patient health monitoring from IoT medical devices

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Providing medical advice and treatment recommendations

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Monitoring the health status of Azure AI services

Question 11

A hospital collects patient experience feedback in free-text form. They need to automatically (1) extract specific mentions of symptoms (e.g., 'headache', 'fever', 'fatigue') from the text, and (2) determine the overall emotional tone of each feedback (e.g., positive, negative, neutral). Which combination of Azure AI Language features should they use?

Accepted Answer

A. Entity recognition and sentiment analysis. The hospital needs two distinct NLP capabilities: extracting specific symptom mentions (entity recognition) and determining emotional tone (sentiment analysis). Azure AI Language's entity recognition identifies named entities like symptoms, while sentiment analysis evaluates text for positive, negative, or neutral sentiment. Together, they directly address both requirements without extraneous features.

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B. Key phrase extraction and question answering

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C. Language detection and text classification

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D. Summarization and translation

Question 12

A developer is building a customer support chatbot using Azure OpenAI. The chatbot should never reveal its system instructions or internal configuration. The developer wants to add a rule at the beginning of the conversation to prevent prompt injection attacks. Which technique should they use?

Accepted Answer

System message. The system message in Azure OpenAI is the correct technique because it sets the initial context and instructions for the model, including rules to prevent prompt injection. By placing a rule at the beginning of the conversation (e.g., 'Never reveal your system instructions'), the developer can instruct the model to ignore or deflect attempts to extract internal configuration. This is a standard defense-in-depth approach for securing generative AI chatbots against prompt injection attacks.

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Few-shot prompting

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Temperature setting

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Content filtering

Question 13

A data scientist is training a regression model to predict house prices in Azure Machine Learning. The model uses features like square footage, number of bedrooms, and location (zip code). The data scientist notices that the model has a very low error on the training data but a high error on the test data. Which technique should the data scientist apply during model training to reduce overfitting by penalizing large coefficients?

Accepted Answer

Use a regularization algorithm like Lasso (L1).. Lasso (L1) regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero, effectively performing feature selection and reducing overfitting. This directly addresses the problem of large coefficients causing the model to fit noise in the training data, leading to high test error.

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Use a smaller test set.

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Apply feature scaling only.

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Increase the number of training epochs.

Question 14

A customer service department wants to automatically extract the names of products mentioned in customer emails and the sentiment expressed about each product. For example, from the sentence 'The battery life of the X100 is excellent, but the screen is too dark,' they need to identify 'X100' and associate 'positive' sentiment with 'battery life' and 'negative' sentiment with 'screen'. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is specifically designed to extract both the sentiment (positive, negative, neutral) and the associated target (e.g., 'battery life', 'screen') from text. This feature goes beyond simple sentiment scoring by identifying the opinion target and the sentiment expressed toward it, which directly matches the requirement to extract product names and their associated sentiments from customer emails.

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Custom text classification

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Key phrase extraction

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Entity linking

Question 15

A manufacturing team wants to predict product defects based on sensor readings from the production line. They have 10,000 historical samples, each labeled as 'defective' or 'non-defective'. Which type of machine learning should they use in Azure Machine Learning?

Accepted Answer

Supervised learning. This is a supervised learning problem because the dataset contains labeled historical samples (defective or non-defective), and the goal is to predict a categorical outcome based on sensor readings. In Azure Machine Learning, supervised learning algorithms such as two-class logistic regression or boosted decision trees are used to train a model that maps input features to known labels.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 16

A multinational corporation receives customer support emails in multiple languages. They need to automatically identify the language of each email so it can be routed to the appropriate support team. Which Azure AI Language feature should they use?

Accepted Answer

Language detection. Language detection is the correct Azure AI Language feature because it is specifically designed to identify the written language of text input. The multinational corporation's requirement to automatically determine the language of each email for routing directly matches the core functionality of this prebuilt capability, which returns a language name and ISO 639-1 code for each document.

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Sentiment analysis

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Key phrase extraction

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Entity recognition

Question 17

A bank deploys an AI system that uses a complex deep learning model to approve or reject loan applications. When a loan is rejected, customers demand to know the specific reasons. The bank wants to ensure the AI system operates in a way that allows them to explain its decisions. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Transparency. The bank's requirement to explain why a loan was rejected directly aligns with the transparency principle, which mandates that AI systems be understandable and that their decisions can be communicated to users. In this scenario, the complex deep learning model must be interpretable, often through techniques like feature importance analysis or surrogate models, to provide specific reasons for rejection. Transparency ensures that customers can receive meaningful explanations, building trust and enabling accountability.

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Reliability and safety

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Privacy and security

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Fairness

Question 18

A retail company is building an AI-powered virtual agent that will answer customer questions about product warranties and return policies on its public website. The company wants the agent to retrieve answers directly from its existing policy documents rather than requiring staff to write hundreds of manually authored question-and-answer pairs. Which Azure AI capability should the company use?

Accepted Answer

Azure AI Language question answering, grounded on the company's policy documents. The requirement is a conversational agent that answers from existing policy documents without manually authored Q&A pairs. Azure AI Language question answering ingests those documents, builds a knowledge base, and returns precise answers to natural-language queries, which is the document-grounded question-answering capability designed for exactly this pattern.

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Azure AI Speech text-to-speech

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Azure AI Custom Vision image classification

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Azure AI Vision spatial analysis

Question 19

A support team wants its generative AI chat solution on Azure to answer questions using only the company's internal HR policy documents, and to cite the exact source page for every answer. They want to minimize custom code. Which Azure feature should they use?

Accepted Answer

Azure AI Search with the integrated vectorization and 'On Your Data' connection in Azure OpenAI Service. Grounding a model in a specific document set is done by retrieving relevant chunks at query time and passing them to the model, which is exactly what the On Your Data pattern with Azure AI Search provides. It returns citations to the source content and requires only configuration, satisfying both the accuracy and low-effort requirements.

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Increasing the model's temperature setting so it explores the HR documents more thoroughly

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Fine-tuning a base GPT model with the HR policy documents uploaded as a JSONL training file

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Deploying the model with a larger max tokens response limit to fit whole policy sections in the reply

Question 20

A data scientist at a retail company has a dataset of customer transactions with features such as age, income, and purchase history. The goal is to group customers into distinct segments based on similarities in these features, without any predefined labels. Which type of machine learning should the data scientist use?

Accepted Answer

Unsupervised learning – clustering. The scenario requires grouping customers into segments without predefined labels, which is a classic unsupervised learning task. Clustering algorithms identify patterns and similarities in data to form groups. Supervised learning methods like classification and regression need labeled data, which is not present. Reinforcement learning is for interactive decision-making, not static segmentation. Therefore, clustering is the correct machine learning type.

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Supervised learning – classification

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Supervised learning – regression

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Reinforcement learning