AI-102 Practice Test 46 — 15 Questions

Question 1

A developer is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The bot needs to handle multiple intents, including 'BookFlight', 'CancelFlight', and 'CheckWeather'. During testing, the bot frequently confuses 'BookFlight' and 'CancelFlight' intents. What is the most effective way to improve intent classification accuracy?

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

Accepted Answer

Add more varied training utterances for 'BookFlight' and 'CancelFlight' intents.. Adding more varied training utterances for the 'BookFlight' and 'CancelFlight' intents directly addresses the root cause of confusion: insufficient or overlapping training data. LUIS relies on diverse utterance patterns to distinguish between semantically similar intents; increasing the quantity and variety of labeled examples improves the model's ability to learn discriminative features, thereby boosting classification accuracy.

Answer

Reduce the number of intents by merging similar ones.

Answer

Increase the confidence threshold for intent predictions.

Answer

Add more entities to the utterances.

Question 2

You are building a knowledge mining solution using Azure AI Search with AI enrichment. Which TWO built-in skills can be used to extract information from images embedded in documents?

Accepted Answer

Image Analysis skill. (Image Analysis skill) is correct because it extracts rich information from images, such as descriptions, tags, captions, and even celebrities or landmarks, using Azure Cognitive Services Computer Vision. This skill is designed to analyze the visual content of images embedded in documents during AI enrichment.

Answer

Entity Recognition skill

Answer

Key Phrase Extraction skill

Answer

Text Translation skill

Question 3

You are deploying an Azure AI Language service custom text classification model. You need to ensure that the training data is balanced and representative. What should you do?

Accepted Answer

Split the labeled data into training and test sets, ensuring each class has similar proportions.. Splitting labeled data into training and test sets while ensuring each class has similar proportions (stratified split) is a standard practice for balanced and representative training data. This approach prevents class imbalance from skewing model evaluation metrics and ensures the model generalizes well to unseen data. In Azure AI Language custom text classification, this is critical for achieving reliable performance across all classes.

Answer

Use only the most frequent labels to train the model.

Answer

Oversample the minority classes to match the majority class size.

Answer

Use all labeled data for training and rely on cross-validation.

Question 4

Your company is deploying an Azure AI Document Intelligence solution to process invoices. The solution must: - Extract key fields (invoice number, date, total amount). - Handle invoices in both PDF and image formats. - Use a prebuilt model to reduce development effort. - Process high volumes (up to 10,000 invoices per day). - Store extracted data in Azure Cosmos DB. You need to design the processing pipeline. What should you do?

Accepted Answer

Use the prebuilt invoice model with the async API. Submit all invoices for analysis. Use an Azure Function to poll for results and write to Cosmos DB.. The prebuilt invoice model's asynchronous API is designed for high-volume batch processing, allowing you to submit up to 10,000 invoices per day without timeout or rate-limit issues. The async API returns operation locations that you can poll via an Azure Function, and once results are ready, you write the extracted fields (invoice number, date, total amount) to Azure Cosmos DB. This decouples submission from retrieval, ensuring scalability and reliability for both PDF and image formats.

Answer

Use the synchronous API of the prebuilt invoice model. For each invoice, call the API and write the result to Cosmos DB.

Answer

Use the prebuilt receipt model to process invoices. Store results in Cosmos DB.

Answer

Use the layout model to extract text from invoices. Then use Azure AI Language to extract entities.

Question 5

You need to extract key phrases from a large collection of customer reviews using Azure AI Language. The solution should be cost-effective and process up to 1,000 documents per day. Which pricing tier should you choose?

Accepted Answer

Standard (S) tier. The Standard (S) tier is the correct choice because it supports up to 1,000 documents per day for key phrase extraction, which matches the stated requirement. The Free (F0) tier is limited to 5,000 text records per month (roughly 167 per day), making it insufficient for 1,000 documents daily. The Premium (P) tier is designed for high-throughput scenarios (millions of documents per day) and is overkill for this volume, while the Basic (B) tier does not exist for Azure AI Language's key phrase extraction API.

Answer

Free (F0) tier

Answer

Premium (P) tier

Answer

Basic (B) tier

Question 6

A financial services firm wants to use Azure OpenAI to generate investment advice summaries. They must ensure that the model does not produce any advice that could be interpreted as personalized financial advice. What is the most effective strategy?

Accepted Answer

Use a system message that instructs the model to avoid personalized advice and apply strict content filtering.. Azure OpenAI's system messages allow you to set the model's behavior and constraints at the conversation level, which is the most direct and effective way to enforce a policy like avoiding personalized financial advice. Combined with Azure's content filtering (which can block harmful or restricted content), this approach provides both instruction-based and filter-based guardrails without requiring model retraining or relying solely on example-based prompting.

Answer

Set temperature to 0 and top_p to 0 to make outputs deterministic.

Answer

Provide few-shot examples of disclaimers in the prompt.

Answer

Fine-tune the model on a dataset of generic financial summaries.

Question 7

Your team uses Azure AI Studio to deploy a large language model endpoint. You need to monitor for prompt injection attacks. Which Azure AI safety feature should you enable?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct feature because it provides built-in detection for prompt injection attacks by analyzing user inputs for malicious content, such as jailbreak attempts or indirect injection patterns. This service integrates directly with Azure AI Studio endpoints to filter harmful prompts before they reach the LLM, ensuring compliance with responsible AI practices.

Answer

Azure AI Agent Service

Answer

Azure AI Search

Answer

Azure OpenAI Service

Question 8

You are using Microsoft Copilot Studio to create an agent that helps users reset their passwords. The agent should first verify the user's identity using multi-factor authentication (MFA) before proceeding. Which feature should you configure?

Accepted Answer

Configure Authentication settings to require Microsoft Entra ID authentication with MFA policy. Microsoft Copilot Studio allows you to configure Authentication settings directly on the agent, and by selecting 'Microsoft Entra ID' as the authentication provider, you can enforce an MFA policy that is already configured in your Entra ID tenant. This ensures that before the agent processes any password reset logic, the user must complete MFA, satisfying the identity verification requirement without custom code or flows.

Answer

Add a variable to store the user's identity status

Answer

Add a Power Automate flow that calls Microsoft Entra ID MFA

Answer

Use a 'Sign in' topic trigger from the customer channel

Question 9

You are a machine learning engineer at a retail company. The company wants to build a product knowledge base by extracting information from product manuals, specifications sheets, and customer reviews. The data sources include PDFs, Word documents, and plain text files stored in Azure Blob Storage. The solution must: (1) extract product name, model number, price, and key features; (2) analyze customer reviews to extract sentiment and common issues; (3) enable natural language queries like 'Which products have the best reviews under $100?'; (4) handle documents in English and Spanish. You need to design a solution using Azure AI Search and Azure AI Services. Which approach meets all requirements with the least development effort?

Accepted Answer

Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.. It uses a single Azure AI Search pipeline with a skillset that includes Document Layout skill (to handle various document formats like PDFs, Word docs, and text files), Text Translation skill (to convert Spanish documents to English, unifying the language), Sentiment skill (to analyze customer reviews for sentiment), and Key Phrase Extraction skill (to extract key features and common issues). Enabling semantic search allows natural language queries such as 'Which products have the best reviews under $100?'. This approach meets all requirements with the least development effort, as it avoids the need for multiple pipelines or custom model training required by other options.

Answer

Use Azure AI Document Intelligence custom model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.

Answer

Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.

Answer

Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.

Question 10

You are planning to use Azure AI Vision to analyze images for a retail inventory management application. The solution must detect products on shelves and read expiration dates. Which two Azure AI Vision capabilities should you use?

Accepted Answer

Object Detection. Object Detection (B) is correct because it identifies and locates products on shelves by drawing bounding boxes around each detected item, which is essential for inventory tracking. Optical Character Recognition (OCR) (D) is correct because it extracts text from images, enabling the reading of expiration dates printed on product labels or packaging.

Answer

Image Captioning

Answer

Face Detection

Question 11

You are building a chatbot using Azure AI Language and need to handle user intents that are not covered by the predefined intents. What should you implement?

Accepted Answer

A 'None' intent in a conversational language understanding project. In a Conversational Language Understanding (CLU) project, the 'None' intent is specifically designed to capture utterances that do not match any of the defined intents. This intent acts as a catch-all for unrecognized user inputs, ensuring the chatbot can gracefully handle out-of-scope or ambiguous queries without misclassifying them into a predefined intent.

Answer

Custom entities to capture unknown phrases

Answer

A fallback intent in the QnA Maker knowledge base

Answer

A prebuilt intent from the LUIS catalog

Question 12

A company uses Microsoft Copilot Studio to build an agent that helps employees find policy documents. The agent needs to answer questions about the employee handbook, which is stored in SharePoint Online. The agent should only respond to queries about the handbook and ignore unrelated questions. Which configuration should the agent designer apply?

Accepted Answer

Disable fallback responses and add a condition to the existing topic.. To ensure the agent only responds to handbook queries and ignores unrelated questions, you must disable fallback responses so that the agent does not provide any default reply for unrecognized inputs. Additionally, add a condition to the handbook topic so that it only triggers on relevant queries. Creating a topic alone (option A) still leaves the system fallback active, causing the agent to respond to unrelated questions with a generic message. Authentication (option D) controls user access but does not limit the scope of responses. Enabling generative answers (option C) would allow the agent to answer a broad range of questions, not restrict it to the handbook.

Answer

Create a topic for handbook queries and configure authentication and security to restrict access.

Answer

Enable generative answers and add the SharePoint site as a data source.

Answer

Set bot-level authentication to require Microsoft Entra ID sign-in.

Question 13

Your Azure AI Search solution uses a custom skill to call an external API. The skill runs locally but fails when deployed to the search service. What is the most likely cause?

Accepted Answer

The skill endpoint is not publicly accessible via HTTPS.. When a custom skill runs locally but fails after deployment to Azure AI Search, the most common cause is that the skill's endpoint is not publicly accessible via HTTPS. Azure AI Search indexers execute skills in the cloud and must be able to reach the external API over the internet using a secure HTTPS connection; localhost or HTTP endpoints will fail.

Answer

The skill's output field mappings are missing.

Answer

The skill's input field mappings are incorrect.

Answer

The indexer name is misspelled in the skillset.

Question 14

You are building a knowledge mining solution that indexes technical manuals in multiple languages. The solution must enable users to search in their native language and retrieve results in the same language. Which approach should you use?

Accepted Answer

Use language-specific analyzers in the Azure AI Search index for each language. Azure AI Search supports language-specific analyzers (e.g., 'de.microsoft' for German, 'fr.microsoft' for French) that apply linguistic rules such as stemming, lemmatization, and stop-word removal tailored to each language. This ensures that queries and documents are processed in the same language, enabling users to search and retrieve results in their native language without translation loss.

Answer

Detect the language of the query using Azure AI Language and then use a generic analyzer

Answer

Translate all queries to English using Azure AI Translator before searching

Answer

Use a single non-language-specific analyzer like 'standard.lucene' for all documents

Question 15

Which TWO of the following are best practices for securing Azure AI services?

Accepted Answer

Enable diagnostic settings to audit usage and detect anomalies.. Enabling diagnostic settings for Azure AI services allows you to collect and analyze logs and metrics, which is essential for auditing usage, detecting anomalies, and monitoring security-related events. This aligns with the security best practice of maintaining visibility into service activity to identify potential threats or misconfigurations.

Answer

Expose endpoints publicly to simplify client access.

Answer

Disable diagnostic logging to reduce data exposure.

Answer

Share API keys among multiple applications for simplicity.