AI-102 Implement generative AI solutions Practice Test 6 — 15 Questions

Question 1

You are using Azure OpenAI Service to summarize customer emails. The summaries must be concise and contain only key information. Which prompt engineering technique should you apply?

Accepted Answer

Use few-shot prompting with examples of desired summaries. Few-shot prompting is the correct technique because it provides the model with explicit examples of desired input-output pairs (e.g., a verbose email and its concise summary). This guides the model to learn the exact format, tone, and level of detail required for the summaries, which is critical for consistency in a production summarization pipeline. Without examples, the model may default to its training distribution and produce overly verbose or irrelevant output.

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Use chain-of-thought prompting

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Use zero-shot prompting with a one-sentence instruction

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Use negative prompting to avoid verbose output

Question 2

You are deploying a generative AI solution that uses DALL-E to generate images. The application must ensure that generated images do not contain violent content. Which feature should you enable?

Accepted Answer

Configure Azure AI Content Safety to moderate images. Azure AI Content Safety is a dedicated service for detecting and moderating harmful content, including violence, in images and text. By integrating this service into your DALL-E image generation pipeline, you can scan generated images for violent content before they are delivered to users, ensuring compliance with safety policies. This is the correct approach because Azure AI Content Safety provides pre-built, customizable content moderation models specifically designed for this purpose.

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Use grounding with Azure AI Search

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Fine-tune the DALL-E model

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Enable content filtering in DALL-E

Question 3

You are building a generative AI application using Azure OpenAI Service. The application must provide citations for answers retrieved from a set of documents. You need to ensure that each answer includes a reference to the source document. Which configuration should you use?

Accepted Answer

Use Azure OpenAI On Your Data with the 'include citations' option. Option A is correct because Azure OpenAI On Your Data provides a built-in 'include citations' feature that automatically retrieves and appends source document references to the generated answer. This configuration leverages the underlying search index to map each response segment back to the original document, ensuring compliance with citation requirements without custom development.

Answer

Add a system message requesting citations

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Implement a custom prompt flow with citation logic

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Fine-tune the model to include citations

Question 4

You are designing a generative AI solution using Azure OpenAI Service. The solution must meet compliance requirements by preventing the model from generating sensitive personal data. Which TWO configurations should you implement? (Select TWO.)

Accepted Answer

Implement data loss prevention (DLP) policies using Microsoft Purview. Microsoft Purview DLP policies can scan and block sensitive data (e.g., PII, credit card numbers) in prompts and responses when integrated with Azure OpenAI Service, ensuring compliance by preventing data exfiltration. Azure AI Content Safety content filters allow you to configure severity thresholds to block harmful or sensitive content categories, directly preventing the model from generating personal data at the inference layer.

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Add a system message instructing the model not to generate personal data

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Deploy the model in a specific region

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Fine-tune the model with a dataset that excludes personal data

Question 5

Your organization is deploying a generative AI chatbot using Azure OpenAI Service. The chatbot must answer questions based on internal documents stored in Azure Blob Storage. You need to implement a retrieval-augmented generation (RAG) solution. Which THREE components are required? (Select THREE.)

Accepted Answer

Azure AI Search index. Option B is correct because Azure AI Search is the core indexing and retrieval engine in a RAG solution. It ingests documents from Azure Blob Storage, creates a searchable index, and enables vector or hybrid search to retrieve relevant chunks. The chatbot then uses these retrieved chunks as context for the Azure OpenAI model to generate grounded answers.

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Azure Functions for preprocessing

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Azure SQL Database for metadata

Question 6

You are using Azure OpenAI Service to generate code snippets. The output must be safe and free of security vulnerabilities. Which TWO practices should you follow? (Select TWO.)

Accepted Answer

Use Azure AI Content Safety to filter outputs. Option C is correct because Azure AI Content Safety is a dedicated service that provides an additional layer of filtering for harmful or inappropriate content, including security vulnerabilities, beyond what the model itself offers. It allows you to define custom severity thresholds and blocklists, ensuring that generated code snippets are safe before they reach the user. This is a recommended practice for production deployments to mitigate risks like injection attacks or exposure of sensitive patterns.

Answer

Increase the temperature parameter to encourage diversity

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Rely on the model's built-in safety features

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Fine-tune the model with a dataset of secure code examples

Question 7

You are using Azure OpenAI Service with the system message shown in the exhibit. The model sometimes answers questions using general knowledge even when the context does not contain the answer. Which modification should you make to enforce the behavior?

Accepted Answer

Set the 'strict' parameter to true in Azure OpenAI On Your Data configuration. Option D is correct because the 'strict' parameter in Azure OpenAI On Your Data configuration forces the model to rely exclusively on the provided data sources and refuse to answer when the context lacks the information. This directly enforces the desired behavior of not falling back on general knowledge.

Answer

Add a user message repeating the instruction

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Reduce the temperature to 0

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Fine-tune the model with examples of refusing to answer

Question 8

You run the Azure CLI command shown in the exhibit to create an online endpoint for a generative AI model. The deployment fails because the selected VM instance type is not available in the East US region. Which action should you take to resolve the issue?

Accepted Answer

Specify a different VM type or region that supports Standard_NC6s_v3. The deployment failed because the Standard_NC6s_v3 VM instance type is not available in the East US region. The correct action is to either choose a different VM type that is available in East US or deploy to a different region that supports Standard_NC6s_v3. This directly addresses the root cause of the failure, as Azure Machine Learning online endpoints require the selected VM SKU to be available in the target region.

Answer

Increase the instance count to 2

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Use a batch endpoint instead of online endpoint

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Change --compute-type to CPU

Question 9

You are testing an Azure OpenAI model with the parameters shown in the exhibit. The model generates very short responses. Which parameter should you modify to allow longer responses?

Accepted Answer

Increase max_tokens. The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When responses are very short, increasing max_tokens allows the model to produce longer completions up to the specified limit. The other parameters affect randomness, diversity, or probability distribution, not the length cap.

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Increase frequency_penalty

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Increase top_p

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Increase temperature

Question 10

You are developing a customer support chatbot using Azure OpenAI Service. The chatbot must only answer questions related to the company's product catalog and policies. You want to minimize the risk of the chatbot generating harmful or off-topic responses. Which approach should you use?

Accepted Answer

Use a system message that instructs the model to only answer product-related questions.. Option B is correct because a system message sets the foundational behavior of the model by providing high-level instructions that guide all subsequent responses. By explicitly instructing the model to only answer product-related questions, you establish a clear boundary that minimizes off-topic or harmful outputs. This approach leverages the model's instruction-following capability, which is more effective than parameter tuning alone for content restriction.

Answer

Set the max_tokens parameter to 100.

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Set the temperature parameter to 0.

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Set the top_p parameter to 0.1.

Question 11

Your team is building an application that uses Azure OpenAI Service to summarize legal documents. You need to ensure that the summaries do not include any personally identifiable information (PII) that might appear in the source documents. Which feature should you configure in the Azure OpenAI Service?

Accepted Answer

Enable content filtering with PII detection.. Option B is correct because Azure OpenAI Service's content filtering system includes built-in PII detection capabilities that can automatically identify and redact personally identifiable information from model inputs and outputs. This feature operates at the platform level, ensuring PII is filtered regardless of how the model is prompted, providing a reliable safeguard for sensitive legal documents.

Answer

Configure rate limiting to reduce processing volume.

Answer

Set the system message to instruct the model to exclude PII.

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Use the 'Add your data' feature to ground the model.

Question 12

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must classify invoices by vendor and extract line items. Which prebuilt model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model (Option A) is specifically designed to extract common fields from invoices, including vendor details and line items, without requiring custom training. This model is optimized for invoice documents and provides out-of-the-box extraction of structured data such as vendor name, invoice date, and line-item descriptions, quantities, and amounts.

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Custom extraction model

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Prebuilt receipt model

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Prebuilt layout model

Question 13

Your company uses Microsoft 365 Copilot to generate meeting summaries. Some users report that summaries include information from meetings they did not attend. What is the most likely cause?

Accepted Answer

Users have access to meeting artifacts via shared calendars or transcripts.. Option B is correct because Microsoft 365 Copilot generates meeting summaries by aggregating content from meeting artifacts such as transcripts, recordings, and shared calendars. If a user has access to a meeting's transcript or recording (e.g., via a shared calendar or because the meeting was recorded and stored in a location the user can access), Copilot can include that meeting's information in summaries even if the user did not attend. This behavior is by design, as Copilot respects existing permissions on the underlying data.

Answer

The meeting organizer granted everyone in the organization view access.

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Copilot is using Bing search results to augment summaries.

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Copilot is incorrectly configured to ignore meeting permissions.

Question 14

You are deploying an Azure OpenAI model for a healthcare application. You need to ensure that the model does not generate medical advice and that all responses include a disclaimer. Which configuration should you use?

Accepted Answer

Configure a system message with instructions and enable content filtering.. Option D is correct because configuring a system message with explicit instructions (e.g., 'Do not provide medical advice; always include a disclaimer') combined with Azure AI Content Safety's content filtering allows you to enforce behavioral guardrails and block harmful outputs at the application layer. The system message sets the model's behavior, while content filtering provides a secondary safety net to catch policy violations, ensuring compliance in a regulated healthcare environment.

Answer

Ground the model with your own medical documents.

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Set max_tokens to 50 to limit response length.

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Use Azure AI Content Safety to filter medical terms.

Question 15

You are building a generative AI solution using Azure OpenAI Service. You want to deploy the model to a specific Azure region to minimize latency for users in Europe. Which deployment parameter must you set?

Accepted Answer

The Azure region of the Azure OpenAI resource.. Option C is correct because the Azure region of the Azure OpenAI resource directly determines the physical data center location where the model is deployed. By selecting a region in or near Europe (e.g., France Central, Sweden Central, UK South), you minimize network round-trip latency for European users. The region is set at the resource creation level and cannot be changed after deployment.

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The model version (e.g., 0613).

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The temperature parameter.

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The deployment name.