AI-102 Practice Exam 60 — 20 Questions

Question 1

Refer to the exhibit. You are configuring an Azure AI Foundry agent for customer support. The agent uses Azure AI Search for retrieval and Azure OpenAI for generation. Users report that the agent provides correct answers but sometimes includes inappropriate language. What is the most likely cause?

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

Accepted Answer

The content safety blocklist is not applied to the chat output. Azure AI Foundry agents rely on Azure OpenAI content filtering to block harmful language in generated responses. If the content safety blocklist is not applied to the chat output, the agent can produce inappropriate language even when the retrieved information is accurate. The blocklist must be explicitly configured to filter the final output of the agent.

Answer

The OpenAI deployment 'gpt-4o' does not support content filtering

Answer

The content safety threshold should be set to 'low' to block more content

Answer

The semantic configuration is not optimized for safety

Question 2

A bank uses Azure AI Document Intelligence to process loan applications. The solution must extract data from scanned PDFs and validate it against a database. The bank requires that all extracted data be encrypted at rest and in transit. Which security measure should you implement?

Accepted Answer

Enable customer-managed keys (CMK) with Azure Key Vault for the Document Intelligence resource. Customer-managed keys (CMK) with Azure Key Vault provide the ability to control the encryption keys used to protect data at rest in Azure AI Document Intelligence. This, combined with the platform's default encryption in transit (TLS), satisfies the requirement for encrypting extracted data both at rest and in transit. CMK is the only option that directly addresses encryption key management for data at rest.

Answer

Use a system-assigned managed identity for the application

Answer

Configure a private endpoint for the Document Intelligence resource

Answer

Use Azure RBAC to restrict access to the Document Intelligence resource

Question 3

Your organization uses Azure OpenAI Service to generate code snippets. You want to log all user prompts and model responses for auditing purposes. What should you configure?

Accepted Answer

Enable diagnostic settings in Azure OpenAI Service to send logs to a Log Analytics workspace.. Azure OpenAI Service's diagnostic settings allow you to stream platform logs—including user prompts and model responses—to a Log Analytics workspace. This is the native, built-in mechanism for capturing and analyzing request/response data for auditing, without needing additional services or custom middleware.

Answer

Use Azure API Management to log requests and responses.

Answer

Store logs in Azure Key Vault.

Answer

Use Azure AI Search to index the prompts and responses.

Question 4

Which TWO actions can reduce the cost of using Azure Custom Vision for image classification? (Choose two.)

Accepted Answer

Use the compact domain for faster training.. Using the compact domain in Azure Custom Vision reduces model complexity and training time, which directly lowers compute costs. Compact domains are optimized for edge deployment and require fewer resources, making them more cost-effective for image classification tasks.

Answer

Include negative samples in the dataset.

Answer

Use a larger image size for higher accuracy.

Answer

Increase the number of training iterations.

Question 5

You are using Azure AI Language's conversational language understanding (CLU). The above JSON is a request to a CLU endpoint. What is the purpose of this request?

Accepted Answer

To predict the intent and entities from the user utterance. The JSON request is sent to the Azure AI Language CLU endpoint with a 'query' field containing the user utterance. The 'kind' field is set to 'Conversation', which triggers the CLU runtime to analyze the utterance against the deployed model. The purpose is to return a prediction of the top intent and any extracted entities, which is the core function of a conversational language understanding endpoint.

Answer

To query a knowledge base for answers

Answer

To deploy the CLU model to production

Answer

To train a new CLU model

Question 6

A PowerShell script (exhibit) attempts to update the endpoint for an Azure AI Language resource. What is the outcome of running this script?

Accepted Answer

The script fails because the property does not exist. The PowerShell script attempts to set the 'endpoint' property on an existing Azure AI Language resource using the `Set-AzCognitiveServicesAccount` cmdlet. However, the 'endpoint' property is read-only and cannot be modified after resource creation; it is generated automatically by Azure based on the resource's name and region. Attempting to update it results in a failure because the property does not exist as a writable attribute in the resource's schema.

Answer

The script creates a new resource

Answer

The script changes the API version

Answer

The script updates the endpoint successfully

Question 7

Your organization needs to analyze customer feedback from social media posts to determine the sentiment (positive, negative, neutral). The solution must process up to 10,000 posts per day and provide a confidence score for each sentiment. Which Azure AI service should you use?

Accepted Answer

Azure AI Language Service. Azure AI Language Service provides pre-built sentiment analysis capabilities that can process up to 10,000 posts per day and return a confidence score for each sentiment (positive, negative, neutral). This service is specifically designed for natural language processing tasks like sentiment analysis, making it the correct choice for analyzing customer feedback from social media posts.

Answer

Azure AI Speech Service

Answer

Azure AI Translator

Answer

Azure AI Language Understanding (LUIS)

Question 8

Refer to the exhibit. You have deployed a GPT-3.5 Turbo model in Azure OpenAI Service with the shown configuration. Users report that the model generates responses that are too repetitive. You need to reduce repetition. Which parameter should you modify?

Accepted Answer

Increase presencePenalty to 0.5. Increasing the presencePenalty parameter penalizes tokens that have already appeared in the generated text, encouraging the model to introduce new topics and reduce repetition. In Azure OpenAI Service, presencePenalty directly influences the logit scores of previously seen tokens, making them less likely to be selected again, which addresses the user's complaint of overly repetitive responses.

Answer

Increase frequencyPenalty to 0.5

Answer

Increase temperature to 1.0

Answer

Decrease topP to 0.5

Question 9

You are implementing a chatbot using Microsoft Copilot Studio that helps employees find company policies. The chatbot must: - Use generative answers based on a SharePoint Online site. - Only respond with information from approved policy documents. - Include citations in responses. - Be accessible from Microsoft Teams. - Require no custom code. What should you do?

Accepted Answer

In Copilot Studio, create a new copilot. Add the SharePoint site as a knowledge source. Enable generative answers with citations. Publish to Teams.. Microsoft Copilot Studio natively supports adding a SharePoint Online site as a knowledge source, enabling generative answers that retrieve and cite only approved policy documents. It requires no custom code, automatically includes citations in responses, and can be published directly to Microsoft Teams, fulfilling all stated requirements.

Answer

Use Power Automate to retrieve documents and feed them to Azure OpenAI. Build a custom connector for Teams.

Answer

Build a bot using Azure Bot Service and QnA Maker. Train it with the policy documents. Deploy to Teams.

Answer

Create a custom GPT in Azure OpenAI Studio. Upload the policy documents. Deploy via Azure API Management and expose to Teams.

Question 10

Which THREE factors should you consider when choosing between Azure AI Document Intelligence prebuilt models and custom models for invoice processing?

Accepted Answer

Prebuilt models require no training data.. Azure AI Document Intelligence prebuilt models are designed to extract common fields from standard invoice layouts without requiring any labeled training data. They are pretrained on a large corpus of documents, enabling immediate use for typical invoice structures.

Answer

Both model types can be deployed on-premises.

Answer

Prebuilt models are always less accurate than custom models.

Question 11

You need to extract key-value pairs from scanned forms as part of a knowledge mining solution. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract key-value pairs, tables, and structured data from scanned forms and documents using prebuilt and custom models. This aligns directly with the requirement for knowledge mining from scanned forms.

Answer

Azure AI Vision

Answer

Azure AI Language

Answer

Azure AI Search

Question 12

You need to provide a business analyst with access to create and manage Azure AI Language projects without granting them full subscription-level permissions. What role should you assign?

Accepted Answer

Cognitive Services Language Owner at the resource level. The Cognitive Services Language Owner role at the resource level grants full permissions to create, read, update, and delete Azure AI Language projects and resources, including managing custom models and deployments, without granting any permissions outside that specific resource. This is the least-privilege role that satisfies the business analyst's need to manage Language projects while avoiding subscription-level access.

Answer

Reader at the resource group level

Answer

Contributor at the subscription level

Answer

Cognitive Services User at the resource level

Question 13

You are an AI engineer at a global e-commerce company. The company uses Azure AI Language to analyze product reviews in English, Spanish, and French. The current solution calls the sentiment analysis API for each review individually, resulting in high latency and cost. You need to design a new solution that processes reviews in batches, reduces the number of API calls, and still supports multiple languages. The solution must also extract key phrases and detect the language automatically. You have the following options:

Option A: Use the Azure AI Language synchronous API with the 'multi-language' parameter set to true. Send reviews one by one.

Option B: Use the Azure AI Language asynchronous batch API. Combine all reviews into a single batch request, but only for one language at a time.

Option C: Use the Azure AI Language asynchronous batch API. Send a single batch request with all reviews, setting the 'language' parameter to 'multi' to auto-detect language, and specify sentiment analysis, key phrase extraction, and language detection as tasks.

Option D: Use the Azure AI Translator service to translate all reviews to English, then use the Azure AI Language batch API for English-only sentiment and key phrase extraction.

Accepted Answer

Option C. The asynchronous batch API supports multiple tasks (sentiment, key phrases, language detection) in a single request, and setting language to 'multi' enables auto-detection. This reduces API calls and latency. Option A is wrong because synchronous one-by-one calls increase latency/cost. Option B is wrong because it processes only one language per batch, requiring multiple batches. Option D is wrong because it adds translation cost and latency, and may lose nuances.

Answer

Option B

Answer

Option A

Answer

Option D

Question 14

A company is building a chatbot using Azure Cognitive Service for Language. They need to ensure that user utterances are correctly mapped to the appropriate intent in a custom question answering project. What should they configure?

Accepted Answer

Add synonyms and phrase list to the custom question answering project.. Adding synonyms and phrase lists to a custom question answering project directly improves the mapping of user utterances to intents by normalizing variations in phrasing. This configuration allows the project to recognize equivalent terms (e.g., 'cost' and 'price') as the same intent, ensuring accurate intent mapping without requiring exact matches.

Answer

Add synonyms and phrase list to the LUIS application.

Answer

Add alternative question phrases to the QnA pairs in the custom question answering project.

Answer

Define entities in the custom question answering project to capture key information.

Question 15

You need to deploy a generative AI model that can be used by multiple applications within your organization. The model must support real-time inference with low latency. Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides managed access to powerful generative AI models like GPT-4, which are optimized for real-time inference with low latency through provisioned throughput units (PTUs) and regional deployment options. This service is specifically designed for generative AI workloads, offering REST API endpoints that support streaming responses and sub-second latency for single-turn interactions, making it ideal for multiple applications requiring consistent, low-latency responses.

Answer

Azure AI Search

Answer

Azure Machine Learning real-time endpoint

Answer

Azure Functions

Question 16

Refer to the exhibit. A developer received this response from an Azure OpenAI chat completion call. The prompt was "What is the capital of France?". The finish_reason is "stop". What does this indicate?

Accepted Answer

The model completed the response naturally.. The finish_reason 'stop' indicates that the model completed the response naturally, meaning it generated a complete answer to the prompt and reached a logical stopping point (e.g., the end of a sentence or the end of the generated text). This is the standard behavior for a successful completion where the model did not encounter any content filter, token limit, or other interruption.

Answer

The response was truncated due to content filtering.

Answer

The model stopped generating before the response was complete.

Answer

The response reached the max_tokens limit.

Question 17

Your company uses Azure AI Vision to analyze images. You receive an alert that the number of 429 (Too Many Requests) errors has increased significantly. What is the most likely cause?

Accepted Answer

The application is exceeding the transactions-per-second (TPS) limit.. HTTP 429 (Too Many Requests) is a rate-limiting response from Azure AI Vision when the client exceeds the allowed transactions-per-second (TPS) for the chosen pricing tier. The alert indicates the application is sending requests faster than the service's capacity, triggering throttling to protect backend resources.

Answer

The endpoint URL is incorrect.

Answer

The API key has expired.

Answer

The service principal does not have the correct role assignment.

Question 18

You are using Azure OpenAI Service to generate marketing copy. You notice that the output sometimes contains factual inaccuracies about your company's products. Which action can you take to improve factual accuracy?

Accepted Answer

Include relevant product information in the system message.. Including relevant product information in the system message provides the model with authoritative context that grounds its responses in factual data. The system message acts as a persistent instruction set that the model uses to shape its outputs, reducing reliance on its internal training data which may be outdated or incomplete. This technique, known as 'grounding,' directly improves factual accuracy by supplying the model with the specific facts it needs to generate correct marketing copy.

Answer

Lower the temperature to 0.

Answer

Increase the maxTokens to 4000.

Answer

Add a stop sequence to limit output.

Question 19

You are deploying a custom text classification model using Azure AI Language. The model must be retrained monthly with new labeled data. You need to automate the retraining process with minimal manual intervention. Which approach should you use?

Accepted Answer

Use the Azure AI Language REST API to trigger training and deployment on a schedule using Azure Logic Apps. Azure Logic Apps can schedule HTTP requests to the Azure AI Language REST API to trigger training and deployment of a custom text classification model. This approach automates the monthly retraining process without manual intervention, using the API's capabilities for model creation, training, and deployment.

Answer

Create an Azure DevOps pipeline that manually retrains the model every month

Answer

Retrain the model manually using Language Studio each month

Answer

Use Azure Machine Learning to host the custom model and automate retraining

Question 20

A company wants to use Azure AI Language to automatically summarize large documents. The summarization must extract the most important sentences from each document. Which feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization selects the most important sentences directly from the source document to create a concise summary, preserving the original wording. This aligns with the requirement to extract key sentences without generating new text, making it the correct choice for this scenario.

Answer

Abstractive summarization

Answer

Key phrase extraction

Answer

Entity recognition