AI-102 Implement generative AI solutions Practice Test 11 — 15 Questions

Question 1

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 2

You are designing a generative AI solution using Azure OpenAI Service. The solution must support multiple languages and provide consistent quality across languages. Which THREE actions should you take?

Accepted Answer

Use a model that supports multiple languages (e.g., GPT-4). GPT-4 is a multilingual model pre-trained on diverse language corpora, enabling it to generate coherent and contextually appropriate responses across many languages without additional fine-tuning. This ensures consistent quality by leveraging the model's inherent cross-lingual capabilities, which is essential for a generative AI solution that must support multiple languages.

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Fine-tune the model on a dataset of a single language

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Set the temperature to 0 for all requests

Question 3

Refer to the exhibit. You are configuring an Azure OpenAI Service deployment for document summarization. The current parameters produce summaries that are often too verbose. You need to make the summaries more concise while maintaining factual accuracy. Which parameter change should you make?

Accepted Answer

Decrease max_tokens to 100. Decreasing max_tokens to 100 directly limits the maximum length of the generated summary, forcing the model to produce shorter output. This addresses the verbosity issue without altering the model's factual accuracy, as max_tokens controls output length, not content selection or creativity.

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Increase top_p to 1.0

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Increase frequency_penalty to 0.5

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Increase temperature to 0.7

Question 4

Refer to the exhibit. You are reviewing the configuration of an Azure OpenAI Service resource. The resource is configured with customer-managed keys for encryption. What is the primary benefit of this configuration?

Accepted Answer

Enhanced control over data encryption keys. Customer-managed keys (CMK) allow you to control and manage the encryption keys used to protect your data at rest in Azure OpenAI Service. This provides enhanced control over who can access the keys, when they are rotated, and how they are stored, which is critical for meeting compliance and security requirements. The primary benefit is not performance, cost, or deployment simplicity, but rather the ability to enforce your own key lifecycle and access policies.

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Simplified deployment process

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Improved model performance

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Reduced operational costs

Question 5

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?

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

Accepted Answer

Set a system message with explicit formatting instructions.. System messages in Azure OpenAI Service allow you to set persistent instructions that guide the model's behavior across the entire conversation. By including explicit formatting rules—such as requiring a bullet list of features—in the system message, you enforce consistent output structure without retraining the model. This approach is efficient, cost-effective, and directly leverages the API's design for controlling response format.

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Fine-tune the model with a dataset containing formatted examples.

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Increase the max_tokens parameter to allow longer outputs.

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Adjust the temperature parameter to a lower value.

Question 6

Your company uses Azure AI Content Safety to moderate user-generated content in a chat application. You need to detect and block sexual content in multiple languages. Which pre-built category should you configure?

Accepted Answer

Sexual. Azure AI Content Safety provides pre-built severity-based categories for content moderation. The 'Sexual' category is specifically designed to detect and block explicit sexual content, including text and images, across multiple languages. This makes it the correct choice for your requirement to moderate sexual content in a chat application.

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Self-harm

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Hate

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Violence

Question 7

You are building a generative AI application using Azure OpenAI Service. The application must provide factual answers based on your company's internal knowledge base. You need to minimize the risk of the model generating incorrect information (hallucinations). Which approach should you take?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with Azure AI Search.. Retrieval-Augmented Generation (RAG) with Azure AI Search grounds the model's responses in your company's internal knowledge base by retrieving relevant documents in real time and injecting them into the prompt. This reduces hallucinations by ensuring the model generates answers based on retrieved facts rather than relying solely on its parametric memory. Azure AI Search provides vector and hybrid search capabilities that efficiently index and query your documents, making RAG the most effective approach for factual accuracy.

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Fine-tune the model on your company's documents.

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Use few-shot prompting with examples of correct answers.

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Set the max_tokens parameter to a low value.

Question 8

Your company uses Microsoft Copilot for Microsoft 365. You need to ensure that Copilot only accesses data from approved SharePoint sites and does not use any other organizational data. What should you configure?

Accepted Answer

Apply sensitivity labels to the approved SharePoint sites.. Sensitivity labels can be configured to restrict Microsoft Copilot for Microsoft 365 to only access content from approved SharePoint sites. By applying a sensitivity label that includes the 'mark content' or 'encrypt content' setting with a specific scope, you can use the 'Microsoft Copilot for Microsoft 365' condition under 'Access control' to block Copilot from processing data from unlabeled or non-approved sites. This ensures Copilot respects the label's policy and excludes all other organizational data.

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Configure Conditional Access policies in Microsoft Entra ID.

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Use Microsoft Purview Data Map to catalog the approved sites.

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Set up data retention policies in Microsoft Purview.

Question 9

You are designing a generative AI solution that uses Azure OpenAI Service. The solution must generate code snippets in Python and JavaScript. You need to ensure the model reliably outputs code in the correct language based on user input. Which approach should you use?

Accepted Answer

Use a system message to specify the desired language.. System messages in Azure OpenAI Service allow you to set the context or behavior of the model, such as specifying the desired programming language for code generation. This approach is lightweight, requires no retraining, and reliably guides the model to output code in the correct language based on the user's request, leveraging the model's existing training on both Python and JavaScript.

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Set the top_p parameter to a low value.

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Fine-tune the model on a dataset of code in both languages.

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Set the temperature to 0 to make the model deterministic.

Question 10

You need to monitor usage and costs of your Azure OpenAI Service deployments. Which Azure tool should you use?

Accepted Answer

Azure Cost Management + Billing. Azure Cost Management + Billing is the correct tool for monitoring usage and costs of Azure OpenAI Service deployments because it provides detailed cost analysis, budget tracking, and usage reports across all Azure services. It allows you to set budgets, create cost alerts, and analyze spending patterns specifically for OpenAI model deployments, including per-model and per-region cost breakdowns.

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Azure Monitor

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Azure Service Health

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Azure Advisor

Question 11

Which TWO actions can you take to mitigate the risk of generating harmful content when using Azure OpenAI Service? (Choose two.)

Accepted Answer

Set a system message that instructs the model to avoid harmful outputs.. Option A is correct because a system message sets the model's behavioral guardrails at inference time, and explicitly instructing the model to refuse or avoid harmful outputs is a documented prompt-engineering mitigation for Azure OpenAI Service. Option D is correct because Azure AI Content Safety filters (the default and customizable content filters in Azure OpenAI) inspect both prompts and completions for categories such as hate, violence, sexual, and self-harm, and block or annotate harmful content before it reaches users. Option B is not correct because fine-tuning on safe examples can shape tone and style but is not a reliable content-safety control and does not replace the platform's content filtering. Option C is not correct because multi-region deployment addresses availability and latency, not the generation of harmful content. Option E is not correct because increasing maxTokens only allows longer responses and can actually increase exposure to harmful output rather than mitigate it.

Answer

Fine-tune the model on a dataset of safe examples.

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Deploy the model in multiple regions.

Answer

Increase the maxTokens parameter to allow longer responses.

Question 12

Which THREE components are required to implement a Retrieval-Augmented Generation (RAG) solution with Azure OpenAI Service? (Choose three.)

Accepted Answer

An embedding model (e.g., text-embedding-ada-002). An embedding model like text-embedding-ada-002 is essential for converting user queries and document chunks into dense vector representations. These vectors enable semantic similarity search in a vector database, which is the core retrieval step in RAG. Without embeddings, the system cannot match user intent to relevant content.

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A fine-tuned model

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Azure AI Content Safety

Question 13

Which TWO Azure AI services can be used to build a conversational chatbot that uses generative AI? (Choose two.)

Accepted Answer

Azure AI Bot Service. Azure AI Bot Service (B) provides a dedicated platform for building, deploying, and managing conversational chatbots, integrating with channels like Microsoft Teams and Slack. Azure OpenAI Service (E) enables generative AI capabilities by providing access to large language models (e.g., GPT-4) that can generate human-like responses, making it ideal for powering the conversational intelligence of a chatbot.

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Azure AI Search

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Azure AI Translator

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Azure AI Language

Question 14

You are reviewing an ARM template for deploying Azure OpenAI Service. The template includes a deployment for gpt-35-turbo with a capacity of 100. You need to ensure that the deployment uses provisioned throughput instead of standard. What should you modify?

Accepted Answer

Change the sku name to 'ProvisionedManaged'.. To use provisioned throughput (PTU) with Azure OpenAI Service, you must set the SKU name to 'ProvisionedManaged' in the ARM template. The default SKU is 'Standard', which uses pay-per-token consumption. Changing the SKU name to 'ProvisionedManaged' tells the resource provider to allocate dedicated throughput capacity for the deployment, ensuring consistent latency and throughput regardless of other workloads.

Answer

Remove the raiPolicyName property.

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Increase the capacity to 200.

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Change the model format to 'GPT-4'.

Question 15

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.

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Lower the temperature to 0.

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Increase the maxTokens to 4000.

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Add a stop sequence to limit output.