AI-900 Practice Exam 63 — 20 Questions

Question 1

What is Azure Machine Learning's 'responsible AI dashboard'?

Accepted Answer

A multi-dimensional model analysis tool covering error analysis, interpretability, and fairness. The responsible AI dashboard in Azure Machine Learning is a comprehensive, multi-dimensional tool that integrates several open-source components (such as Error Analysis, InterpretML, and Fairlearn) to help data scientists and developers evaluate and improve their models across error analysis, interpretability, and fairness dimensions. It is designed to operationalize responsible AI practices by providing a single pane of glass for debugging model behavior, understanding feature importance, and detecting potential fairness issues.

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A legal compliance checklist for AI regulations in different countries

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A monitoring dashboard for tracking API usage and costs

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A tool for documenting model cards for AI transparency

Question 2

What does 'human-in-the-loop' data labeling mean in Azure Machine Learning?

Accepted Answer

Using ML to pre-label data while routing uncertain cases to human reviewers for quality assurance. In Azure Machine Learning, 'human-in-the-loop' data labeling combines ML model pre-labeling with human review for uncertain cases. This approach improves efficiency by automating easy labels while ensuring quality and accuracy through human oversight on ambiguous or low-confidence predictions, directly supporting active learning workflows.

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Replacing all human data labelers with ML models

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Requiring all data to be labeled by humans without any ML assistance

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Using a loop in Python code to automate the labeling process

Question 3

What is 'AI bias' and how can it harm individuals in high-stakes decisions?

Accepted Answer

Systematic unfair outcomes for demographic groups caused by biased training data or design choices. AI bias refers to systematic and unfair outcomes that disproportionately affect certain demographic groups, often resulting from biased training data, flawed design choices, or improper feature selection. In high-stakes decisions such as loan approvals, hiring, or criminal sentencing, such bias can lead to discrimination, reinforce societal inequalities, and cause real harm to individuals by denying them opportunities or subjecting them to unjust treatment.

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When a model's predictions consistently favour one output class due to class imbalance

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When an AI model performs worse on unseen test data than on the training data

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The tendency of users to trust AI recommendations over their own judgment

Question 4

A developer uses Azure OpenAI to generate customer support responses. The developer wants to ensure that the model does not produce responses that contain offensive, hateful, or harmful language, even when users input problematic prompts. Which Azure OpenAI feature should the developer configure to achieve this?

Accepted Answer

Enabling the content filter. The content filter in Azure OpenAI is specifically designed to detect and block offensive, hateful, or harmful language in both user prompts and model responses. By enabling this feature, the developer ensures that even if a user submits a problematic input, the model's output will be filtered to prevent generating inappropriate content. This directly addresses the requirement to avoid harmful language.

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Setting a low temperature value

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Limiting the max_tokens parameter

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Setting a high frequency penalty

Question 5

What is 'Azure AI Vision's image moderation' and what content categories does it detect?

Accepted Answer

Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores. Azure AI Vision's image moderation is specifically designed to detect sexually explicit (adult) and suggestive (racy) content in images, returning confidence scores for each category. This is a core feature of the computer vision service that helps platforms comply with content policies by classifying inappropriate visual content rather than modifying images or checking for copyright violations.

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Moderating the resolution and quality of user-uploaded images for platform standards

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Modifying images to blur or remove inappropriate elements automatically

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Detecting copyright violations in user-uploaded images by comparing to known copyrighted works

Question 6

A news agency publishes hundreds of articles daily. They want to automatically extract the main topics discussed in each article, such as 'politics', 'economy', or 'sports', to categorize content without manual tagging. Which built-in Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it identifies the main topics or subjects discussed in a document, such as 'politics', 'economy', or 'sports', without requiring manual tagging. This feature returns a list of key phrases that represent the core content of each article, directly addressing the need to automatically categorize content by topic.

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Named entity recognition

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Sentiment analysis

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Language detection

Question 7

What is Azure AI Search (formerly Cognitive Search) and how does it relate to generative AI?

Accepted Answer

An enterprise search service used in RAG to retrieve relevant documents for LLM context. Azure AI Search is an enterprise search service that indexes and retrieves relevant documents from your own data sources. In the context of generative AI, it is a core component of the Retrieval Augmented Generation (RAG) pattern, where it provides the LLM with up-to-date, domain-specific context to ground its responses, preventing hallucinations and ensuring factual accuracy.

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A service that generates answers using only the language model's built-in training knowledge

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A tool for searching through Azure OpenAI model configurations

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A database service for storing generated AI content

Question 8

A multinational company receives customer feedback in multiple languages. They need to automatically determine the language of each piece of feedback before routing it to the appropriate support team. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Language Detection. The Language Detection feature in Azure AI Language is specifically designed to identify the language of a given text input, returning the language name and a confidence score. This directly matches the requirement to automatically determine the language of customer feedback before routing it to the appropriate support team.

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Sentiment Analysis

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Key Phrase Extraction

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Named Entity Recognition (NER)

Question 9

What is 'Azure AI Language Studio's evaluation' tab and what metrics does it report?

Accepted Answer

Performance metrics (precision, recall, F1, confusion matrix) on held-out test data for custom models. The 'Evaluation' tab in Azure AI Language Studio is specifically designed to assess the performance of custom models (e.g., custom text classification, custom named entity recognition) against a held-out test dataset. It reports standard classification metrics such as precision, recall, F1 score, and a confusion matrix, which are essential for measuring model accuracy and identifying misclassifications.

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A tab showing the evaluation scores given by users to the AI's responses in production

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Environmental evaluation showing the compute carbon footprint of model training

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A compliance evaluation checklist verifying the model meets data privacy requirements

Question 10

A developer is using Azure OpenAI Service to classify customer support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. The developer provides three labeled examples for each category in the prompt to improve the model's accuracy. What technique is the developer applying?

Accepted Answer

Few-shot learning. Few-shot learning is the correct technique because the developer is providing a small number of labeled examples (three per category) directly in the prompt to guide the model's output without updating the model's weights. This approach leverages the model's in-context learning ability, where the examples act as a pattern for the model to follow when classifying new tickets.

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Fine-tuning

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Prompt engineering

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Retrieval-augmented generation

Question 11

A social media platform wants to automatically generate alternative text descriptions for images posted by users to improve accessibility for visually impaired users. Which Azure Computer Vision capability should be used?

Accepted Answer

Image Captioning. Image Captioning is the correct capability because it generates human-readable descriptions of image content, which directly meets the requirement to produce alternative text for accessibility. Unlike other options, it synthesizes a complete sentence describing the scene, objects, and actions, making it ideal for screen readers.

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Optical Character Recognition (OCR)

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Object Detection

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Face Detection

Question 12

Match each Azure AI service to its pricing model.

Accepted Answer

Azure Cognitive Services: Pay-as-you-go per transaction. Azure AI services generally use pay-as-you-go or consumption-based pricing. Cognitive Services charge per transaction, Azure Machine Learning charges for compute and workspace, Bot Service charges per message, and Cognitive Search charges per index and queries. Be careful not to confuse these models.

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Azure Cognitive Services: Flat monthly fee

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Azure Machine Learning: Consumption-based per message

Question 13

A retail company wants to segment its customers into different groups based on purchasing behavior, without using predefined categories. Which type of machine learning task should they use?

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique that groups data points based on inherent similarities without requiring predefined labels. In this scenario, the retail company wants to discover natural segments in customer purchasing behavior, such as high-frequency buyers or discount seekers, without providing any existing categories. Azure Machine Learning offers clustering algorithms like K-Means, which iteratively assigns customers to clusters by minimizing within-cluster variance based on features like purchase frequency and average order value.

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Classification

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Regression

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Reinforcement learning

Question 14

A customer service team wants to build an Azure AI-powered bot that can understand the intent behind customer messages. For example, the bot should recognize that 'I want to return my shoes' maps to a 'ReturnItem' intent, and 'Where is my order?' maps to 'TrackOrder'. Which Azure service provides pre-built models specifically for intent recognition?

Accepted Answer

Language Understanding (LUIS). Language Understanding (LUIS) is the correct Azure service because it provides pre-built models and custom capabilities specifically designed for intent recognition and entity extraction from natural language utterances. The scenario requires mapping customer messages like 'I want to return my shoes' to a 'ReturnItem' intent, which is exactly the core function of LUIS—it analyzes user input to identify the user's goal (intent) and any relevant details (entities).

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Text Analytics

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Translator Text

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Speech-to-text

Question 15

What can Azure AI Vision's spatial analysis feature do?

Accepted Answer

Analyze video to detect people's presence and movement in physical spaces. Azure AI Vision's spatial analysis feature is designed to analyze video streams from cameras to detect the presence and movement of people in physical spaces. It uses computer vision models to track individuals, count occupancy, and understand movement patterns in real-time, enabling applications like retail analytics or workplace safety.

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Extract text from documents and images

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Identify the 3D coordinates of objects in satellite imagery

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Generate 3D models from 2D photographs

Question 16

What is 'GitHub Copilot' and how does it relate to Azure OpenAI?

Accepted Answer

An AI IDE extension that generates code suggestions in real time, powered by Azure OpenAI models. GitHub Copilot is an AI-powered code completion tool integrated as an extension in IDEs like Visual Studio Code. It generates real-time code suggestions based on the context of the code being written, and it is powered by OpenAI's Codex model, which runs on Azure OpenAI Service. This makes option B correct because it accurately describes Copilot as an AI IDE extension that uses Azure OpenAI models.

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A physical robot assistant that helps GitHub employees with coding tasks

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A version control tool that automatically merges code branches using AI

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A GitHub Actions workflow that runs AI-powered code review on every pull request

Question 17

A marketing team uses Azure OpenAI to generate product descriptions. They want the output to reflect their latest catalog and current pricing, not the model's general knowledge. Which technique should they use?

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is the correct technique because it allows the model to retrieve up-to-date information from an external knowledge base—such as the latest catalog and current pricing—and incorporate that data into the generated output. Unlike the model's static training data, RAG dynamically injects fresh, domain-specific content at inference time, ensuring accuracy and relevance without modifying the model itself.

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Few-shot learning

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Fine-tuning

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Prompt engineering

Question 18

What is 'speculative decoding' and how does it improve LLM inference speed?

Accepted Answer

Using a small draft model to generate candidate tokens that a large model verifies in parallel — improving throughput. Speculative decoding improves LLM inference speed by using a small, fast draft model to generate multiple candidate tokens in sequence, which are then verified in parallel by the large target model. This parallel verification allows the large model to accept or reject entire blocks of tokens at once, significantly reducing the number of sequential autoregressive steps required. The technique leverages the observation that draft models can produce acceptable continuations most of the time, and the large model only needs to correct mistakes, leading to higher throughput without sacrificing output quality.

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Predicting user input before they finish typing to pre-compute responses

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Generating speculative forecasts about future events using language model knowledge

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Running model inference on the CPU while the GPU processes the next request in parallel

Question 19

A company uses Azure OpenAI Service to generate long technical reports. To manage costs, the development team needs to accurately estimate the number of tokens that a given prompt will consume before making any API call. Which Azure OpenAI Service feature should they use to obtain this estimate?

Accepted Answer

The Token Counter tool in Azure OpenAI Studio. The Token Counter tool in Azure OpenAI Studio is specifically designed to estimate the number of tokens a prompt will consume before making an API call. This allows developers to predict costs accurately by calculating token usage for both input and expected output, without incurring actual API charges.

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The Chat Completions API

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The Embeddings API

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The Content Filter configuration

Question 20

What is 'speech synthesis markup language' (SSML) in Azure AI Speech?

Accepted Answer

An XML language for controlling TTS speech rate, pitch, pauses, and emphasis. SSML is an XML-based markup language that allows you to fine-tune text-to-speech (TTS) output by controlling prosodic elements such as speech rate, pitch, volume, pauses, and emphasis. In Azure AI Speech, SSML tags like , , and are embedded in the input text to produce more natural and expressive synthesized speech.

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A markup language for styling how text is displayed on screen in Azure apps

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A security standard for marking sensitive spoken content for redaction

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A standard for describing the structure of multi-turn dialogue in speech interfaces