AI-900 Practice Exam 62 — 20 Questions

Question 1

What is 'Azure Machine Learning's job submission' and what types of training jobs are supported?

Accepted Answer

Submitting training scripts to managed compute — command, sweep, pipeline, and AutoML job types. Azure Machine Learning's job submission is the process of sending a training script to a managed compute target for execution. The supported job types are command (running a script), sweep (hyperparameter tuning), pipeline (multi-step workflows), and AutoML (automated model selection and training). This makes option B correct because it accurately lists these four job types.

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Submitting applications to join the Azure AI Engineering team at Microsoft

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Submitting model predictions as batch jobs to process large datasets overnight

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Scheduling when model monitoring jobs run to check for data drift

Question 2

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 3

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 4

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 5

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 6

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 7

A legal firm needs to automatically extract custom entities such as 'Party Name', 'Effective Date', and 'Governing Law' from contracts. They have a set of manually labeled contracts to use as training data. Which Azure AI Language feature should they use?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows the legal firm to train a model on their manually labeled contracts to extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law'. Unlike built-in NER, which only recognizes predefined entity types, Custom NER learns from the provided labeled data to identify custom categories tailored to the firm's needs.

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Key Phrase Extraction

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Entity Linking

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Built-in Named Entity Recognition

Question 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

A company uses an AI system to help screen job applications. The system ranks candidates based on their resumes. The company wants to ensure that if a candidate asks why they were not selected, the company can provide a clear explanation of the factors that influenced the AI's decision. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the company needs to provide a clear explanation of why a candidate was not selected, which directly aligns with transparency's goal of making AI decisions explainable to users.

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Accountability

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Privacy and security

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Reliability and safety

Question 16

A healthcare research organization publishes an AI system that diagnoses skin conditions from images. In a study, they discover that the model's accuracy is significantly lower for people with darker skin tones compared to those with lighter skin tones. According to Microsoft's Responsible AI principles, which principle most directly requires the organization to disclose this limitation in their documentation?

Accepted Answer

Transparency. The Transparency principle requires AI systems to be understandable and for their limitations to be clearly communicated. In this scenario, the organization must disclose the model's lower accuracy for darker skin tones because users and clinicians need to know when the system is less reliable to make informed decisions. Without this disclosure, the system could be misused or trusted inappropriately, violating the core tenet of transparency.

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Fairness

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Accountability

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Privacy and Security

Question 17

What is a training job in Azure Machine Learning?

Accepted Answer

A single execution of a training script that produces a trained model and tracked metrics. A training job in Azure Machine Learning is a single execution of a training script that runs on a specified compute target, producing a trained model and logging metrics, parameters, and artifacts. This is the fundamental unit of model training in Azure ML, distinct from batch inference or data preprocessing.

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A batch prediction job that scores new data against a deployed model

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A scheduled report on model performance in production

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A data preprocessing pipeline that cleans raw datasets

Question 18

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 19

What type of machine learning model is used for time series forecasting?

Accepted Answer

Sequential models (like LSTM, ARIMA) that learn patterns in historical time-ordered data to predict future values. Time series forecasting relies on sequential models like LSTM (a type of recurrent neural network) or ARIMA (AutoRegressive Integrated Moving Average) that explicitly capture temporal dependencies, trends, and seasonality in historical data ordered by time. These models learn patterns from past observations to predict future values, making them the standard approach for tasks such as stock price prediction or demand forecasting.

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K-means clustering to group similar time periods together

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Image classification models applied to chart images

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Decision trees that map dates to outcomes

Question 20

What is 'clustering' in unsupervised machine learning?

Accepted Answer

Grouping similar data points together without predefined labels based on natural patterns. Clustering is an unsupervised learning technique that automatically groups data points based on inherent similarities or patterns in the data, without requiring any pre-existing labels. The algorithm identifies natural structures, such as distance or density relationships, to form clusters. In Azure Machine Learning, clustering is commonly implemented using algorithms like K-Means or DBSCAN for tasks such as customer segmentation or anomaly detection.

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Classifying data points into predefined categories using labelled training examples

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Grouping Azure compute resources together for distributed training jobs

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Organising model training runs into logical groups for experiment tracking