AI-102 Implement generative AI solutions — Timed 25 Questions

Question 1

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.

Answer

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 2

You are deploying a generative AI application using Azure OpenAI Service. The application must generate responses in multiple languages while maintaining high accuracy. You need to minimize token usage. Which approach should you recommend?

Accepted Answer

Use a base model with a system message to output in the desired language. Azure OpenAI Service base models (e.g., GPT-4) natively support multilingual generation via a system message that sets the desired output language. This approach avoids the overhead of translation pipelines or fine-tuning, directly minimizing token usage while maintaining high accuracy through the model's inherent multilingual capabilities.

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Translate all input to English and then translate output back

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Fine-tune a model for each target language

Answer

Use a separate deployment for each language

Question 3

Which THREE components are required to build a custom copilot using Microsoft Copilot Studio that can answer questions from a SharePoint document library?

Accepted Answer

A Microsoft Copilot Studio copilot. A Microsoft Copilot Studio copilot is the core conversational interface that orchestrates the interaction. Without the copilot itself, there is no runtime environment to process user queries, manage conversation state, or invoke configured knowledge sources. It is the mandatory host for all custom copilot functionality.

Answer

A Power Automate flow to trigger the copilot

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An Azure OpenAI Service deployment

Question 4

You are developing a generative AI solution that uses Azure OpenAI Service. You need to control the creativity of the generated responses. Which parameter should you adjust?

Accepted Answer

temperature. The temperature parameter directly controls the randomness of token selection in the model's output. Lower values (e.g., 0.2) make the model more deterministic and focused, while higher values (e.g., 0.8) increase creativity and variability. This is the primary parameter for adjusting creativity in Azure OpenAI Service.

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top_p

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max_tokens

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frequency_penalty

Question 5

You are designing a generative AI solution that uses Azure OpenAI Service. The solution must not generate responses that include personally identifiable information (PII). Which TWO configurations should you implement? (Choose two.)

Accepted Answer

Configure the system message to instruct the model not to generate PII.. A system message can instruct the model to avoid generating PII, which is a recommended practice. Option E is correct: fine-tuning the model on a dataset that excludes PII reduces the likelihood of PII generation, though it is not foolproof. Option A is incorrect because Azure AI Content Safety does not have built-in PII detection; PII detection is in Azure AI Language, not directly applicable to Azure OpenAI output filtering. Option B (diagnostic logging) only audits, not prevents. Option C (data retention) does not affect output content.

Answer

Use Azure AI Content Safety to filter PII in the output.

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Enable diagnostic logging to audit all generated responses.

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Set data retention policies to delete prompts and completions after 30 days.

Question 6

Your organization is deploying a generative AI solution using Azure AI Foundry. The solution must comply with responsible AI principles, including fairness and transparency. Which combination of tools should you use to assess and mitigate bias in the model?

Accepted Answer

Azure AI Content Safety and Azure Machine Learning fairness assessment. Option A. Azure AI Content Safety can be used to detect and filter harmful content, helping ensure fairness and transparency. Azure Machine Learning's fairness assessment evaluates and mitigates bias in models. Option B is incorrect because Azure AI Language PII detection focuses on personally identifiable information, not bias, and Azure AI Search is for indexing and search. Option C is incorrect because Microsoft Defender XDR is a security tool unrelated to AI fairness. Option D is incorrect because Microsoft Purview Data Map is for data governance, and Azure AI Document Intelligence processes documents, not bias.

Answer

Azure AI Language PII detection and Azure AI Search

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Microsoft Defender XDR and Azure AI Language

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Microsoft Purview Data Map and Azure AI Document Intelligence

Question 7

A company wants to generate personalized product descriptions for its e-commerce site using Azure OpenAI. They need to ensure the model's output adheres to brand guidelines and does not generate prohibited content. Which approach should they use?

Accepted Answer

Use a system message with brand guidelines and apply content filtering.. Using a system message allows you to embed brand guidelines directly into the conversation context, instructing the model on tone, style, and prohibited content. Azure OpenAI's content filtering provides an additional safety layer by automatically detecting and blocking harmful or policy-violating outputs, ensuring compliance with both brand and regulatory requirements.

Answer

Use prompt engineering with negative prompts and ignore content filtering.

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Provide few-shot examples in the user message and rely on the model's training.

Answer

Fine-tune the model with brand guidelines and disable content filtering for performance.

Question 8

A healthcare startup is developing a chatbot that uses Azure OpenAI to answer patient questions. They need to ensure that the chatbot only uses information from their verified medical database and does not generate unsupported medical advice. What is the best approach?

Accepted Answer

Use Azure AI Search with vector search to retrieve relevant documents and pass them as context.. It uses Azure AI Search with vector search to retrieve only relevant, verified documents from the medical database and passes them as context to the Azure OpenAI model. This grounds the model's responses in authoritative data, preventing it from generating unsupported medical advice. The retrieval-augmented generation (RAG) pattern ensures the chatbot answers are based on the provided context rather than the model's internal knowledge.

Answer

Fine-tune a model on the medical database and deploy it.

Answer

Embed the entire medical database in the system message.

Answer

Rely on Azure OpenAI's content filtering to block unsupported advice.

Question 9

A developer wants to deploy a custom generative AI model using Azure Machine Learning. Which compute target should they choose for low-latency real-time inference?

Accepted Answer

Azure Kubernetes Service (AKS). Azure Kubernetes Service (AKS) is the correct compute target for low-latency real-time inference because it supports horizontal pod autoscaling, GPU acceleration, and can be configured with a low-latency ingress controller (e.g., NGINX or Azure Application Gateway) to route inference requests directly to model containers. AKS also integrates with Azure Machine Learning's real-time inference endpoint, which uses a gRPC or HTTP-based scoring protocol to achieve sub-100ms response times.

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Local deployment

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Azure Batch

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Azure Functions

Question 10

A company uses Azure OpenAI to generate code snippets. They notice that the model sometimes produces code that uses deprecated APIs. They want to minimize this without retraining the model. What should they do?

Accepted Answer

Add a system message instructing the model to use only current, non-deprecated APIs.. Adding a system message in Azure OpenAI allows you to set high-level instructions that guide the model's behavior without retraining. By explicitly instructing the model to use only current, non-deprecated APIs, you leverage the system prompt's ability to influence output style and content, effectively reducing deprecated API usage in generated code snippets.

Answer

Fine-tune the model on a dataset of recent code.

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Set the temperature parameter to 0 to reduce randomness.

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Provide a few-shot example of correct code in the prompt.

Question 11

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.

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Provide few-shot examples of disclaimers in the prompt.

Answer

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

Question 12

A developer wants to use Azure OpenAI to generate text from a prompt. Which parameter controls the diversity of the generated output?

Accepted Answer

temperature. Temperature is the parameter that directly controls the randomness or diversity of the generated output by scaling the logits before applying the softmax function. A higher temperature (e.g., 1.0) increases the probability of less likely tokens, producing more creative and varied responses, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused.

Answer

presence_penalty

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frequency_penalty

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max_tokens

Question 13

A company is using Azure OpenAI to generate customer support responses. They want to ensure the model does not use any personally identifiable information (PII) in its outputs. What should they implement?

Accepted Answer

Use Azure AI Content Safety to filter PII from the output.. Azure AI Content Safety provides built-in PII detection and redaction capabilities that can automatically scan and filter sensitive information from model outputs. This is the most reliable approach because it operates as a post-processing filter, catching PII that the model might generate despite instructions. Fine-tuning, prompt engineering, and system messages are all fallible because they rely on the model's compliance rather than enforced filtering.

Answer

Fine-tune the model on anonymized data.

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Use prompt engineering to instruct the model to redact PII.

Answer

Use a system message instructing the model to avoid PII.

Question 14

A research lab wants to use Azure OpenAI to generate synthetic data for training a model. They need to generate a large volume of data quickly and cost-effectively. Which approach should they use?

Accepted Answer

Use the batch API to process requests asynchronously.. The batch API is designed for high-throughput, asynchronous processing of large volumes of requests, making it ideal for generating synthetic data at scale. It allows the lab to submit many prompts in a single batch, which Azure OpenAI processes efficiently, reducing both cost and time compared to real-time processing.

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Fine-tune a model to generate the data locally.

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Use the streaming API with multiple concurrent connections.

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Deploy a model on Azure Functions and call it in parallel.

Question 15

Which TWO actions should you take to reduce the cost of using Azure OpenAI for a chatbot that handles high traffic?

Accepted Answer

Use the batch API for non-real-time requests.. The batch API allows you to submit non-real-time requests that are processed asynchronously, often at a lower cost per token compared to real-time inference. This is ideal for chatbot scenarios where some queries (e.g., historical analysis or bulk processing) do not require immediate responses, reducing overall Azure OpenAI consumption costs.

Answer

Increase max_tokens to reduce the number of requests.

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Lower the temperature to 0 to reduce token usage.

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Fine-tune the model to reduce prompt length.

Question 16

Which THREE factors should you consider when selecting a model for a generative AI solution on Azure?

Accepted Answer

Cost per token and deployment options.. Cost per token directly impacts operational expenses in pay-per-use models like GPT-4 on Azure OpenAI, and deployment options (e.g., global vs. regional, provisioned throughput) affect availability and scalability. These are fundamental considerations when operationalizing a generative AI solution.

Answer

Number of transformer layers in the model.

Answer

Training data source and licensing.

Question 17

Which TWO Azure services can be used together with Azure OpenAI to implement a Retrieval-Augmented Generation (RAG) solution?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is correct because it can serve as a vector database for storing and querying embeddings generated by Azure OpenAI. When combined with Azure AI Search, which provides hybrid search (vector + keyword) and indexing capabilities, it enables a complete RAG pipeline where relevant documents are retrieved and passed as context to the OpenAI model for grounded generation.

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Azure Blob Storage

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Azure SQL Database

Answer

Azure Functions

Question 18

You are a machine learning engineer at a large retail company. The company has thousands of product descriptions that need to be updated regularly. They currently use a manual process. You propose using Azure OpenAI to generate new descriptions based on product attributes. You have a dataset of existing product descriptions and attributes stored in an Azure SQL Database. The solution must be cost-effective, scalable, and must not require retraining the model. You need to design the solution. You have the following options:

Option A: Use Azure OpenAI with few-shot learning by including examples in the prompt for each product. Deploy the model on an Azure Kubernetes Service (AKS) cluster for high throughput.

Option B: Use Azure OpenAI with prompt templates that include product attributes and call the API for each product. Use Azure Logic Apps to orchestrate the workflow and store results back to Azure SQL Database.

Option C: Fine-tune a custom model on the existing product descriptions and deploy it as a managed endpoint. Use Azure Data Factory to batch process all products.

Option D: Use Azure OpenAI with the batch API to generate descriptions for all products at once, using a single prompt that lists all products and attributes. Store the batch output in Azure Blob Storage and then import into Azure SQL Database.

Which option should you choose?

Accepted Answer

Option B. (Azure Logic Apps) is the correct choice. It uses Azure OpenAI with prompt templates that insert product attributes, making individual API calls per product. This approach is scalable because Azure Logic Apps can handle high volumes with built-in retry and concurrency, and it is cost-effective as you only pay per API call and execution. It does not require model retraining. In contrast, Option A (AKS) introduces unnecessary infrastructure complexity; Option C (fine-tuning) requires retraining; and Option D (batch API) risks exceeding prompt size limits and is less suitable for incremental updates.

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Option C

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Option D

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Option A

Question 19

You are a cloud solution architect at a legal firm. The firm needs to automate the summarization of legal documents. They have a large corpus of past case summaries and legal documents stored in Azure Blob Storage. They want to use Azure OpenAI to generate summaries for new documents. The solution must ensure that the generated summaries are accurate and do not contain hallucinated legal facts. The firm also requires that the solution be serverless and minimize operational overhead. You need to design the solution.

Option A: Use Azure OpenAI with a system message that instructs the model to be accurate. Deploy the model as a web app on Azure App Service and call it from Azure Functions triggered by new blob uploads.

Option B: Use Azure OpenAI with Retrieval-Augmented Generation (RAG) by indexing the past case summaries in Azure AI Search. Use Azure Functions to process new documents, retrieve relevant cases, and pass them as context to the model. Store summaries in Azure Cosmos DB.

Option C: Fine-tune an Azure OpenAI model on the past case summaries and deploy it as a managed endpoint. Use Azure Logic Apps to trigger summarization when new blobs are added.

Option D: Use Azure OpenAI with the chat API and provide the entire document in the prompt. Use Azure Container Instances to run a service that calls the API and writes summaries back to Blob Storage.

Which option should you choose?

Accepted Answer

Option B. It uses Retrieval-Augmented Generation (RAG) with Azure AI Search to ground the model's output in verified past case summaries, directly addressing the requirement to avoid hallucinated legal facts. The serverless architecture is achieved via Azure Functions triggered by blob uploads, minimizing operational overhead, while storing summaries in Azure Cosmos DB provides a scalable, low-latency output store.

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Option A

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Option D

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Option C

Question 20

A company is building a chatbot using Azure OpenAI Service to answer customer queries. The chatbot must not generate harmful or offensive content. Which Azure AI service should be integrated to filter inappropriate content?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it provides built-in content moderation APIs that detect and filter harmful or offensive text and images, including hate speech, violence, self-harm, and sexual content. Integrating this service with the Azure OpenAI chatbot ensures that user inputs and model outputs are screened in real time, preventing the generation of inappropriate responses.

Answer

Azure Bot Service

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Azure Cognitive Search

Answer

Azure Form Recognizer