AI-102 Implement generative AI solutions Practice Test 13 — 15 Questions

Question 1

You are implementing a RAG (Retrieval-Augmented Generation) solution using Azure AI Search and Azure OpenAI Service. The solution is returning answers that are not relevant to the user query. What is the most likely cause?

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

Accepted Answer

The relevance score threshold is set too low.. A low relevance score threshold in Azure AI Search allows documents with low semantic or vector similarity to be returned as results. When these poorly matched documents are passed to Azure OpenAI Service for answer generation, the model may produce answers that are not relevant to the user query, as the retrieved context is noisy or unrelated.

Answer

The max_tokens parameter is set too high.

Answer

The chunk size is too small.

Answer

The index includes too many documents.

Question 2

You are developing a generative AI application that must comply with responsible AI principles. Which Azure AI service should you use to detect and filter harmful content in both input prompts and output responses?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the dedicated Azure service for detecting and filtering harmful content such as hate speech, violence, self-harm, and sexual content in both user prompts and AI-generated responses. It provides configurable severity levels and integrates directly with generative AI workflows to enforce responsible AI policies, making it the correct choice for this requirement.

Answer

Microsoft Purview

Answer

Azure OpenAI Service

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Azure AI Language

Question 3

You are deploying a solution that uses Azure OpenAI Service to generate financial reports. You need to ensure the outputs are accurate and consistent. Which TWO parameters should you adjust? (Choose two.)

Accepted Answer

Set temperature to 0.. Setting temperature to 0 makes the model deterministic, always choosing the most likely next token. This is critical for financial reports where consistency and reproducibility are required, as it eliminates randomness in the output.

Answer

Set presence_penalty to 0.5.

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Set max_tokens to 2000.

Answer

Set frequency_penalty to 0.7.

Question 4

You are building a generative AI chatbot using Microsoft Copilot Studio. The chatbot must answer questions from a PDF document and a SQL database. Which THREE data sources can you configure? (Choose three.)

Accepted Answer

Custom connector to SQL database. A is correct because Microsoft Copilot Studio supports custom connectors to access external data sources like SQL databases. This allows the chatbot to query the SQL database directly using the connector's API, enabling real-time data retrieval for generative AI responses.

Answer

Azure AI Search index

Answer

Azure Blob Storage

Question 5

Your company wants to build a custom Copilot for customer support using Microsoft Copilot Studio. The support team needs to query a backend CRM system securely using the Copilot. Which authentication method should you configure for the custom connector to the CRM?

Accepted Answer

Microsoft Entra ID OAuth 2.0. Microsoft Entra ID OAuth 2.0 is the correct authentication method because it provides secure, token-based delegated access to the backend CRM system without exposing long-lived credentials. Copilot Studio connectors that require user context and secure resource access should use OAuth 2.0 with Microsoft Entra ID, which supports modern authentication flows like authorization code grant and can enforce conditional access policies.

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API key authentication

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Basic authentication

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Client certificate authentication

Question 6

You are deploying a conversational AI chatbot using Azure AI Language service. The chatbot must be able to switch between multiple intents in a single conversation without restarting the session. Which feature should you enable?

Accepted Answer

Orchestration workflow. Orchestration workflow in Azure AI Language service allows a chatbot to switch between multiple intents within a single conversation by routing requests to different language models or custom question answering knowledge bases. This enables the chatbot to handle diverse intents seamlessly without restarting the session, as each intent can be processed by the most appropriate component.

Answer

Active learning

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Dynamic entity extraction

Answer

Prebuilt domain components

Question 7

Refer to the exhibit.

{
  "content_filters": [
    {
      "type": "hate",
      "action": "block",
      "severity": "high"
    },
    {
      "type": "sexual",
      "action": "block",
      "severity": "medium"
    },
    {
      "type": "self_harm",
      "action": "block",
      "severity": "low"
    }
  ]
}

You deploy an Azure OpenAI model with the above content filter configuration. A user submits a prompt that the system rates as "hate" at severity level "medium". What happens?

Accepted Answer

The prompt is allowed because the severity is below the threshold.. The content filter configuration blocks 'hate' content only at severity level 'high'. Since the user's prompt was rated as 'hate' at severity 'medium', it falls below the configured threshold and is allowed. Azure OpenAI content filters evaluate severity levels (low, medium, high) and apply the configured action only when the detected severity meets or exceeds the specified threshold.

Answer

The prompt is blocked because hate content is detected.

Answer

The prompt is blocked because the severity is medium.

Answer

The prompt is allowed because the hate filter is not configured for medium.

Question 8

You are developing a custom chatbot using Azure AI Bot Service and Language Understanding (CLU). The chatbot needs to escalate to a human agent when the user's sentiment is negative. Which component should you use to detect sentiment?

Accepted Answer

Azure AI Language sentiment analysis. Azure AI Language sentiment analysis is the correct component because it provides pre-built sentiment detection capabilities that analyze text and return sentiment labels (positive, negative, neutral) and confidence scores. This directly meets the requirement to detect negative user sentiment in chatbot conversations, enabling escalation to a human agent when needed.

Answer

Azure Cognitive Search

Answer

Orchestration workflow

Answer

QnA Maker

Question 9

You are implementing a generative AI chat solution on Azure OpenAI Service. The solution must ground its answers in a large internal knowledge base and return inline citations that link back to the exact source chunks. You want to minimize custom code and use a managed retrieval pipeline. Which Azure OpenAI feature should you configure?

Accepted Answer

Use the On Your Data feature with an Azure AI Search index and set the citation output to include document references.. Grounding answers in private data with verifiable citations requires a retrieval pipeline. Azure OpenAI On Your Data with an Azure AI Search index provides managed ingestion, chunking, retrieval, and citation output, so answers link to the actual source chunks. Prompt instructions or model tuning alone cannot retrieve documents or guarantee accurate references.

Answer

Deploy a fine-tuned model trained on the internal knowledge base and use it for chat completions.

Answer

Set the temperature parameter to a low value and enable content filtering on the deployment.

Answer

Create a system message that instructs the model to answer only from the knowledge base and to include source names.

Question 10

You are designing a generative AI assistant that must produce deterministic, repeatable answers for a compliance workflow. The assistant uses Azure OpenAI chat completions and must return the same output for identical prompts. Which parameter setting should you apply?

Accepted Answer

Set temperature to 0 and top_p to 1.. Deterministic output requires minimizing sampling randomness. Temperature 0 selects the most probable token each step, and top_p 1 disables nucleus truncation, so identical inputs produce nearly identical outputs. Creative temperatures, penalties, or length and streaming controls do not provide repeatability and can introduce variation across runs.

Answer

Set frequency_penalty to 2 and presence_penalty to 2.

Answer

Increase the max_tokens value and enable streaming responses.

Answer

Set temperature to 1 and top_p to 0.95.

Question 11

You are building a generative AI application with Azure OpenAI Service. The application must use your own product catalog data stored in an Azure AI Search index to ground model responses. You need to configure the model deployment so that retrieved documents are automatically injected into the prompt without you manually assembling the context in application code. What should you configure on the model deployment?

Accepted Answer

Add a 'data source' configuration (your Azure AI Search index) to the model deployment by using the Azure OpenAI 'On Your Data' feature.. The Azure OpenAI On Your Data feature lets you attach a supported data source, such as an Azure AI Search index, directly to a model deployment. At inference time the service performs retrieval, injects the relevant chunks into the prompt, and can return citations. This avoids writing custom retrieval and prompt-assembly logic while grounding responses in the product catalog.

Answer

Increase the deployment's tokens-per-minute quota so that the entire product catalog can be sent in each request.

Answer

Enable content filtering on the deployment to restrict responses to catalog topics.

Answer

Set the model temperature to 0 so the model always returns deterministic answers from the catalog.

Question 12

You are developing a generative AI application that uses Azure OpenAI Service. The application must generate responses that are grounded in a specific set of documents stored in Azure Blob Storage. You want to use the simplest approach that allows the model to reference these documents without building a custom retrieval pipeline. What should you use?

Accepted Answer

Azure OpenAI On Your Data with Azure Blob Storage as the data source.. Azure OpenAI On Your Data is a managed feature that integrates with Azure Blob Storage, enabling the model to retrieve and cite documents without custom code. It handles indexing and retrieval automatically, making it the simplest solution for grounding responses in a specific document set. Other options require more effort or are not designed for this purpose.

Answer

Fine-tune the model with the documents and deploy the fine-tuned model.

Answer

Implement a custom RAG pipeline using Azure Cognitive Search and the Chat Completions API.

Answer

Use the Completions API with a prompt that includes the full text of all documents.

Question 13

You are configuring an Azure OpenAI deployment for a generative AI solution that summarizes long legal contracts. Users report that summaries sometimes omit clauses near the end of documents. The documents are up to 120 pages. You need to improve completeness without changing the model. What should you do?

Accepted Answer

Split each document into overlapping chunks, summarize each chunk, and then combine the summaries in a final aggregation step.. Long documents can exceed the model context window, causing later sections to be dropped. Chunking with overlap preserves boundary content, and summarizing chunks before aggregating covers the whole document. Output length, temperature, and streaming do not affect how much input text the model can attend to, so they cannot fix missing clauses.

Answer

Increase the max_tokens parameter of the completion request to its maximum value.

Answer

Enable streaming responses so the client receives partial summaries as they are generated.

Answer

Raise the temperature so the model explores more of the document content.

Question 14

You are deploying a generative AI assistant with Azure OpenAI Service. Company policy requires that the assistant must never output profanity, and that any attempt to elicit such content must be blocked at the service level rather than filtered in application code. You need the least administrative effort to enforce this. What should you do?

Accepted Answer

Create a custom content filter with a blocklist for profanity terms and associate it with the deployment.. Custom content filters in Azure OpenAI let you define blocklists of specific terms and apply them to a deployment. Terms in the blocklist are blocked at the service level, which aligns with the policy that prohibited content must be stopped by the service rather than by application code. Associating the filter with the deployment enforces the rule for all calls.

Answer

Add a system message instructing the model to never use profanity.

Answer

Enable Azure API Management policies to inspect and rewrite responses containing profanity.

Answer

Set the model temperature to 0 and top_p to 1 to reduce the chance of profanity.

Question 15

You are building a generative AI solution with Azure OpenAI Service that uses the GPT-4 model. The solution must process user requests and call external APIs to retrieve real-time data. You need to ensure the model can invoke the correct API based on user intent and return the results in a structured format. Which feature should you implement?

Accepted Answer

Function calling with the 'tools' parameter in the Chat Completions API.. Function calling is the native feature in Azure OpenAI that allows the model to request invocation of external functions with structured arguments. By defining tools, the model can determine when to call an API and with what parameters. This provides reliable integration with external systems and returns structured responses, fulfilling the requirement.

Answer

Prompt engineering with few-shot examples of API calls.

Answer

Using the Assistants API with code interpreter enabled.

Answer

Fine-tuning the model on a dataset of API calls and responses.