AI-102 Practice Test 36 — 15 Questions

Question 1

Your team is developing an AI-powered document summarization solution using Azure OpenAI. You need to ensure that the solution complies with Microsoft's Responsible AI principles, specifically transparency. Which configuration should you implement?

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

Accepted Answer

Add a system message that informs users the summary is generated by AI.. Transparency under Microsoft's Responsible AI principles requires that users are aware when they are interacting with an AI system. Adding a system message that explicitly states the summary is AI-generated fulfills this disclosure requirement. Diagnostic logging (A) aids in accountability and debugging but does not directly inform the user. Fine-tuning (B) improves accuracy but does not address transparency. Content filtering (C) mitigates harmful outputs but does not disclose AI involvement.

Answer

Configure diagnostic logging to capture all model inputs and outputs.

Answer

Fine-tune the model on a custom dataset to improve accuracy.

Answer

Enable content filtering with severity levels high and medium.

Question 2

You are building a generative AI chatbot using Microsoft Copilot Studio. The chatbot must answer questions from a PDF document and a SQL database. Which THREE data sources can you configure? (Choose three.)

Accepted Answer

Custom connector to SQL database. A is correct because Microsoft Copilot Studio supports custom connectors to access external data sources like SQL databases. This allows the chatbot to query the SQL database directly using the connector's API, enabling real-time data retrieval for generative AI responses.

Answer

Azure AI Search index

Answer

Azure Blob Storage

Question 3

You are planning a multi-region deployment of Azure AI services to ensure high availability and low latency for global users. You need to decide how to manage API keys and endpoint URLs across regions. What is the recommended approach?

Accepted Answer

Use Azure API Management to expose a single endpoint that routes to regional Azure AI services resources, each with its own key stored in Key Vault.. It combines Azure API Management as a unified gateway with regional Azure AI Services resources, each secured by individual keys stored in Azure Key Vault. This architecture provides global load balancing, regional failover, and centralized key management without exposing keys in application code. API Management can route requests based on latency or geography, ensuring high availability and low latency while Key Vault rotates keys securely.

Answer

Create separate Azure AI services resources per region and hardcode the keys in application configuration.

Answer

Create a single Azure AI services resource in one region and share the key across all applications.

Answer

Use Microsoft Entra ID authentication with managed identities and have each application call the region-specific endpoint directly.

Question 4

You are designing an AI solution that uses Azure OpenAI Service to answer customer queries. The solution must ensure that the model does not generate harmful or inappropriate content. Which Azure AI service should you configure to enforce content safety policies?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the dedicated service for detecting and filtering harmful content such as hate speech, violence, self-harm, and sexual content. It provides configurable severity thresholds and can be integrated with Azure OpenAI Service to enforce content safety policies on model inputs and outputs. This ensures the AI solution meets responsible AI requirements without modifying the underlying model.

Answer

Azure AI Speech

Answer

Azure OpenAI Service

Answer

Azure AI Search

Question 5

Refer to the exhibit. You are using Azure AI Document Intelligence with a layout model. The pipeline returns an empty tables array even though the document contains tables. The OCR step extracts text correctly. What is the most likely issue?

Accepted Answer

The layout extraction step is not correctly identifying table structures.. The layout model in Azure AI Document Intelligence performs OCR and then uses a layout extraction step to identify structural elements like tables. If the OCR extracts text correctly but the tables array is empty, it indicates that the layout extraction step failed to detect the table boundaries or cell structure, not that OCR missed the text. Option D correctly identifies this as the most likely issue.

Answer

The OCR step is not recognizing table cells.

Answer

The table extraction step is misconfigured.

Answer

The output field mapping for tables is missing.

Question 6

You run the above Azure CLI command. What is the expected output?

Accepted Answer

The primary and secondary keys. The Azure CLI command `az cognitiveservices account keys list` retrieves only the primary and secondary API keys for the Cognitive Services account. The endpoint is not included in the output; it must be obtained separately using `az cognitiveservices account show`. Therefore, the expected output contains the primary key and secondary key only.

Answer

The primary and secondary keys along with the endpoint

Answer

A list of endpoints for the service

Answer

An error because the command is incorrect

Question 7

A company wants to analyze customer reviews to determine whether sentiment is positive, negative, or neutral. The solution must also extract key phrases such as 'great battery life' and 'poor camera quality'. Which Azure AI feature should be used?

Accepted Answer

Azure AI Language - Sentiment Analysis and Key Phrase Extraction. Azure AI Language's Sentiment Analysis and Key Phrase Extraction are specifically designed to evaluate text for positive, negative, or neutral sentiment and to extract meaningful phrases like 'great battery life' or 'poor camera quality'. This combined capability directly matches the dual requirement of sentiment classification and key phrase extraction in a single API call, using pre-built models that require no custom training.

Answer

Azure AI Language - Named Entity Recognition (NER)

Answer

Azure AI Content Safety

Answer

Azure AI Language Understanding (LUIS)

Question 8

You are deploying a real-time translation service using Azure AI Translator. The solution must support automatic language detection and translation for customer chat conversations. Which pricing tier should you select to minimize costs while meeting the requirement?

Accepted Answer

Standard S1. The Standard S1 tier (D) is correct because it provides up to 1 million characters per month for translation and language detection, which is sufficient for typical customer chat workloads. The Free tier (F0) is limited to 2,000 characters per request and 2 million characters per month, which may be insufficient for production chat volumes. Higher tiers (S2, S3) offer increased throughput and character limits but incur higher costs, making S1 the most cost-effective choice that meets the requirement.

Answer

Free (F0)

Answer

Standard S3

Answer

Standard S2

Question 9

Your company uses Azure AI Language to analyze customer feedback. The solution currently uses the default endpoint and key. Security policy requires that all API calls be authenticated using Microsoft Entra ID and that network access be restricted to a specific virtual network. You need to reconfigure the resource. What should you do?

Accepted Answer

Disable local authentication, enable managed identity, and configure the virtual network. It addresses both security requirements: disabling local authentication ensures that API keys cannot be used, forcing all calls to authenticate via Microsoft Entra ID; enabling a managed identity provides a secure identity for the resource to authenticate with Entra ID; and configuring the virtual network restricts network access to the specified VNet, meeting the network restriction policy.

Answer

Regenerate the API keys and update the application

Answer

Create a private endpoint for the resource

Answer

Configure the resource to use Microsoft Entra ID authentication only

Question 10

You deploy an agent in Microsoft Foundry that uses a custom skill in Azure AI Search. The skill calls an Azure Function to enrich documents. The function uses an API key. The deployment succeeds but the skill returns an error when processing documents. The function logs show the request is received but the API key is missing. What is the most likely cause?

Accepted Answer

The skill definition does not include the apiKey in the header.. The custom skill definition in Azure AI Search must include an `apiKey` property in the header to authenticate requests to the Azure Function. Since the function logs show the request is received but the API key is missing, the most likely cause is that the skill definition omitted the `apiKey` header. This is a common configuration error when setting up custom skills with HTTP-triggered functions.

Answer

The Azure Function is not running.

Answer

The API key stored in Azure Key Vault is expired.

Answer

The custom skill's URI is incorrect.

Question 11

Your organization deploys an Azure AI Foundry solution for a customer service chatbot. The chatbot uses a large language model (LLM) hosted on Azure OpenAI Service with a GPT-4 model. Requirements: (1) The chatbot must only use information from the company's internal knowledge base, not general internet knowledge. (2) Responses must include citations from the knowledge base. (3) The solution must filter out any toxic or harmful content. (4) The chatbot must be deployed in a secure environment with network isolation. You have an Azure AI Foundry project with a connected Azure OpenAI resource. The knowledge base is stored in Azure AI Search. You need to configure the solution. What should you do?

Accepted Answer

Use Azure OpenAI on your data with Azure AI Search as the data source, enable content filtering, and configure the model to use the search index with strict grounding.. It uses Azure OpenAI on your data with Azure AI Search as the data source, which ensures the model only retrieves and generates responses from the indexed knowledge base, meeting the requirement to avoid general internet knowledge. Enabling content filtering satisfies the toxicity requirement, and configuring strict grounding ensures responses include citations from the search index. The secure environment with network isolation is achieved through Azure AI Foundry's managed network capabilities, which are compatible with this configuration.

Answer

Use Azure OpenAI with function calling to retrieve knowledge base documents, and enable content filtering.

Answer

Use prompt engineering with system messages to restrict knowledge, and enable content filtering.

Answer

Fine-tune the GPT-4 model on the knowledge base and deploy with content filtering.

Question 12

Refer to the exhibit. You are creating an Azure AI Search index. You want to enable semantic search using the 'content' field as both the title and the content. However, you receive an error that semantic search cannot be configured. What is the most likely reason?

Accepted Answer

The 'contentFields' list is empty; semantic search requires at least one content field or keywords field.. Semantic search in Azure AI Search requires at least one field specified in the 'contentFields' list to provide the textual content for semantic ranking. If the 'contentFields' list is empty, the service cannot extract semantic meaning from the index, resulting in the configuration error. The 'content' field can serve as both title and content if properly assigned, but the error indicates the list itself is missing entries.

Answer

The 'content' field is of type 'Edm.String', which is not supported for semantic search.

Answer

The 'content' field is not searchable.

Answer

The index does not have a sortable field for the title.

Question 13

Refer to the exhibit. You send this request to the Conversational Language Understanding API. The response includes the intent 'BookFlight' with entities 'FromCity: Seattle' and 'ToCity: Boston', but the 'Date' entity is missing. What is the most likely cause?

Accepted Answer

The model was not trained to recognize date entities. The Conversational Language Understanding (CLU) API returns only intents and entities that the deployed model was explicitly trained to recognize. If the training data did not include labeled 'Date' entities, the model will not extract them regardless of the input text. The API itself supports entity extraction, and the endpoint and string index type settings do not affect whether a specific entity type is recognized.

Answer

The stringIndexType should be 'Utf16CodeUnit'

Answer

The API version does not support entity extraction

Answer

The endpoint is pointing to the wrong deployment

Question 14

A company uses Azure AI Language's conversational language understanding (CLU) to build a customer support bot. They want to integrate the bot with Microsoft Teams and need to ensure that user authentication is handled by Microsoft Entra ID. However, users report that the bot sometimes fails to respond when they are not signed into Microsoft Entra ID. What is the most likely cause?

Accepted Answer

The bot's authentication settings in Azure Bot Service require Microsoft Entra ID, but the bot is not passing the token correctly.. The bot's authentication settings in Azure Bot Service require Microsoft Entra ID tokens for user authentication, but the bot is not passing the token correctly. When users are not signed into Microsoft Entra ID, the bot fails to respond because it cannot validate the user's identity via the missing or malformed token. The CLU service itself does not require tokens for prediction; the issue lies in the bot's authentication flow, not the CLU endpoint configuration.

Answer

The CLU model requires a Microsoft Entra ID token for prediction.

Answer

The CLU project endpoint is not configured to accept anonymous requests.

Answer

The bot is not registered in Microsoft Entra ID.

Question 15

Which TWO capabilities are provided by the Azure AI Language service?

Accepted Answer

Custom text classification.. Custom text classification is a core capability of the Azure AI Language service, enabling users to build and deploy custom models that classify text into user-defined categories. This feature is part of the service's suite of custom natural language processing (NLP) capabilities, distinct from pre-built features like sentiment analysis or key phrase extraction.

Answer

Text translation.

Answer

Speech-to-text conversion.

Answer

Image captioning.