AI-102 Implement generative AI solutions Practice Test 2 — 15 Questions

Question 1

A company is developing a generative AI solution that must process sensitive customer data. They need to ensure that data remains within their Azure tenant and is not used to improve the base model. Which configuration is required in Azure OpenAI?

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

Accepted Answer

Opt out of abuse monitoring and data logging in the Azure OpenAI Studio.. Opting out of abuse monitoring and data logging in Azure OpenAI Studio ensures that sensitive customer data is not sent to Microsoft for review or used to improve the base model. This configuration is specifically designed for customers who need to process sensitive data while maintaining data residency within their Azure tenant, as it disables the default logging and human review processes that could expose data outside the tenant.

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Enable customer-managed keys for encryption.

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Use a private endpoint to restrict access to the service.

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Configure data residency to keep data in a specific region.

Question 2

A developer uses the Azure OpenAI API to generate code. They want to ensure that the generated code is in Python. Which parameter should they set?

Accepted Answer

system message. The system message parameter in the Azure OpenAI API is used to set the behavior and context of the assistant, including specifying the desired output format or language. By setting the system message to something like 'You are a helpful assistant that always generates Python code,' the developer can instruct the model to produce Python code consistently. This is the correct parameter for guiding the model's response style and content.

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temperature

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max_tokens

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top_p

Question 3

You need to translate a document from English to Spanish using Azure OpenAI. Which parameter should you include in the prompt to specify the target language?

Accepted Answer

user message. To specify the target language in an Azure OpenAI translation prompt, you include the instruction within the user message (e.g., 'Translate the following English text to Spanish: ...'). The user message is the primary input where the model receives the task and context, making it the correct parameter for language specification.

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max_tokens

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temperature

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system message

Question 4

A company uses Azure OpenAI to generate product descriptions. They want to ensure that the descriptions are consistent in style and tone. Which strategy should they use?

Accepted Answer

Provide a few examples of desired style in the prompt (few-shot learning).. Few-shot learning (option A) is the correct strategy because it directly addresses the need for consistent style and tone by providing the model with explicit examples of the desired output within the prompt. This guides the model to mimic the given patterns without altering the base model's weights, making it a quick and effective method for controlling output style in Azure OpenAI.

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Set max_tokens to a small value to limit output length.

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Fine-tune the model on a dataset of product descriptions.

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Increase the temperature to 1.0 for more creativity.

Question 5

You are building a multi-tenant application that uses Azure OpenAI. Each tenant has different content filtering requirements. How should you configure the solution to meet these requirements?

Accepted Answer

Create separate Azure OpenAI deployments for each tenant with their own content filter configurations.. Azure OpenAI content filters are configured at the deployment level, not at the application or API-call level. By creating separate deployments for each tenant, you can assign distinct content filter configurations (e.g., severity thresholds for hate, violence, self-harm) that are enforced server-side by Azure's content filtering service, ensuring isolation and compliance with each tenant's requirements.

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Use a single Azure OpenAI deployment and configure content filters at the application level.

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Configure different system messages for each tenant to enforce content policies.

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Pass the tenant ID in the API call and use a custom middleware to apply filters.

Question 6

Your company wants to use Azure OpenAI to generate code snippets. You need to ensure the generated code does not contain security vulnerabilities. Which approach should you take?

Accepted Answer

Include a system message that instructs the model to generate secure code following best practices.. Azure OpenAI's system messages allow you to set the context and behavior of the model, including instructing it to follow secure coding best practices (e.g., OWASP Top 10). This is the most direct and scalable way to influence the model's output at inference time without requiring post-processing or retraining. The system message acts as a persistent instruction that guides every response, making it the recommended approach for steering content generation.

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Scan the generated code with a static analysis tool after generation.

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Use content filters to block malicious code.

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Fine-tune the model on a dataset of secure code examples.

Question 7

You need to monitor and audit usage of Azure OpenAI Service to ensure compliance with company policies. Which TWO actions should you take?

Accepted Answer

Configure content filtering and review the filtered content logs.. Content filtering logs capture all requests that are blocked or filtered by Azure OpenAI's content filters, providing an audit trail of policy violations. Option B is correct because enabling diagnostic settings to stream logs to a Log Analytics workspace allows centralized collection and analysis of all API call logs, which is essential for auditing usage. Option C is wrong because Azure Monitor metrics provide aggregated data (e.g., token consumption) but do not capture detailed per-request audit logs required for compliance. Option D is wrong because Azure Policy enforces rules (e.g., restricting model deployments) but does not provide usage auditing or monitoring of API calls. Option E is wrong because Microsoft Purview focuses on data governance and lineage, not on monitoring or auditing Azure OpenAI usage.

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Use Azure Monitor metrics to track token consumption.

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Apply Azure Policy to restrict model deployments.

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Connect Azure OpenAI to Microsoft Purview for data lineage.

Question 8

You are deploying a generative AI model using Azure Machine Learning. The model is a large language model that requires GPU compute. You need to minimize costs while ensuring the model is always available for inference. Which compute option should you choose?

Accepted Answer

Managed online endpoint with a GPU VM. A managed online endpoint with a GPU VM is the correct choice because it provisions a dedicated GPU instance that remains always active, ensuring the model is available for real-time inference at any time. This option balances cost and availability by allowing you to choose a lower-cost GPU SKU (e.g., NCas_v4) while avoiding the cold-start latency of serverless or batch options. The managed endpoint also handles auto-scaling and load balancing, but for constant availability, a fixed minimum instance count is required.

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Batch endpoint with GPU

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Serverless GPU compute

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CPU-based inference

Question 9

You are building a generative AI application that uses Azure OpenAI Service. The application must access data from an Azure SQL database to answer user questions. You need to implement a solution that retrieves the most relevant data without exposing the database schema to the model. Which approach should you use?

Accepted Answer

Use a middle-tier service to query the database, then pass results to the model via Azure OpenAI On Your Data. It uses a middle-tier service to query the Azure SQL database and then passes only the relevant results to the Azure OpenAI model via the 'On Your Data' feature. This approach ensures the model never sees the database schema, protecting sensitive structural details while still providing the necessary context for answering user questions. It also allows the middle-tier to handle authentication, query optimization, and data filtering securely.

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Configure the model to generate SQL queries and execute them against the database

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Embed the entire database schema in the system prompt

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Fine-tune the model with historical query results from the database

Question 10

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 11

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. 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.

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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 12

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 13

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. 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.

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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 14

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.. 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.

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Configure rate limiting to reduce processing volume.

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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 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.. 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.