AI-102 Practice Test 16 — 15 Questions

Question 1

You are developing an agentic solution that uses Azure AI Search as a knowledge base. The agent needs to retrieve the most relevant documents based on a user query. You notice that the agent sometimes returns irrelevant results. Which configuration should you adjust?

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

Accepted Answer

Increase the 'top' parameter to retrieve more candidate documents. Increasing the 'top' parameter retrieves more candidate documents from Azure AI Search, which gives the agent a larger pool of potentially relevant results to rank and filter. This can help when the initial set of top documents misses relevant content, as the agent can then apply its own reasoning or additional reranking to select the best matches. The 'top' parameter controls the number of search results returned, not the relevance scoring itself.

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Change the language analyzer to a different language

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Disable semantic ranking

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Decrease the minimum search score threshold

Question 2

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 3

You are designing a solution that must extract specific entities from customer emails, such as product names, order numbers, and dates. The solution must be able to learn from a small set of labeled examples and improve over time. Which Azure AI service should you use?

Accepted Answer

Custom Entity Extraction. Custom Entity Extraction (D) is correct because it allows you to train a model with a small set of labeled examples to extract domain-specific entities like product names, order numbers, and dates from customer emails. This service supports iterative learning and improvement over time, making it ideal for scenarios where pre-built models lack the required specificity.

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QnA Maker

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Pre-built Entity Extraction

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Text Analytics for health

Question 4

You are a data scientist at a healthcare company. You have deployed a GPT-4 model using Azure OpenAI to answer patient inquiries about medical conditions. The model is configured with temperature=0.3 and max_tokens=200. Recently, the compliance team flagged that some responses contain contradictory information compared to the official medical guidelines. You need to ensure the model's answers align strictly with the provided medical documents (stored as PDFs in Azure Blob Storage). You have access to Azure Cognitive Search and Azure AI Document Intelligence. The solution must minimize hallucinations and not require retraining the model. What should you do?

Accepted Answer

Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.. It uses Azure Cognitive Search to index the medical PDFs and then configures the Azure OpenAI deployment with 'Add your data' to retrieve relevant context from that index at inference time. This retrieval-augmented generation (RAG) approach grounds the model's answers in the official documents without retraining, directly addressing the compliance team's requirement to align responses with the provided guidelines and minimize hallucinations.

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Use prompt engineering to add a system message that tells the model to only answer based on the uploaded PDFs. Keep the current deployment.

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Fine-tune GPT-4 on the medical documents using Azure OpenAI fine-tuning capabilities. Use the fine-tuned model for the chatbot.

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Deploy Azure AI Content Safety to filter responses that contradict guidelines. Set up a custom content filter using a list of approved phrases.

Question 5

Refer to the exhibit. You are calling the Azure AI Language API for entity linking. What is the primary purpose of this request?

Accepted Answer

To identify entities in the text and link them to a knowledge base.. The request is configured for entity linking, which is a specific capability of the Azure AI Language API that identifies named entities in the text and resolves them to a unique identifier in a knowledge base (such as Wikipedia or a custom knowledge graph). This goes beyond simple named entity recognition (NER) by providing a canonical link, enabling disambiguation of entities with the same name (e.g., 'Washington' as a state vs. a person). The response includes both the entity name and a URL to the knowledge base entry, confirming the primary purpose is linking to a knowledge base.

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To extract named entities from the text without linking.

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To extract key phrases from the text.

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To analyze the sentiment of the text.

Question 6

Refer to the exhibit. You are using Azure OpenAI to build a trivia bot. The bot responds correctly to simple questions but sometimes gives overly verbose or off-topic answers. Which parameter should you adjust to make responses more focused?

Accepted Answer

Decrease temperature. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and focused. For a trivia bot that gives overly verbose or off-topic answers, lowering temperature (e.g., from 0.7 to 0.3) forces the model to choose higher-probability tokens, which typically correspond to more concise and relevant responses.

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Increase temperature

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Decrease top_p

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Increase max_tokens

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Decrease max_tokens

Question 7

Which TWO actions should you perform to ensure that an Azure AI Search indexer can successfully enrich documents using a custom skill that calls an external API?

Accepted Answer

Configure a retry policy in the skillset definition for the custom skill. A custom skill in an Azure AI Search skillset can fail due to transient errors when calling an external API. Configuring a retry policy in the skillset definition allows the indexer to automatically retry failed skill executions, improving resilience. Option C is correct because using a managed identity for the search service eliminates the need to manage credentials when accessing an Azure Function, providing secure authentication without storing secrets.

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Enable CORS on the Azure Function app to allow cross-origin requests

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Set the indexer's execution timeout to unlimited

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Add the external API endpoint to the indexer's allowed domains list

Question 8

Which TWO Azure services can be used to perform optical character recognition (OCR) on documents? (Select two.)

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is correct because it provides prebuilt models specifically designed for extracting text, tables, and key-value pairs from documents using OCR as a core component. It uses advanced deep learning models to analyze scanned documents and images, making it a dedicated service for document OCR tasks.

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Azure AI Metrics Advisor

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Azure AI Language

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Azure AI Personalizer

Question 9

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?

Accepted Answer

Opt out of abuse monitoring and data logging in the Azure OpenAI Studio.. Azure OpenAI provides a data privacy setting that allows customers to opt out of abuse monitoring and data logging. When enabled, this ensures that prompts, completions, and any associated data are not stored or used by Microsoft to improve the base model, and all data remains within the customer's Azure tenant. This is the specific configuration required to meet the stated data residency and non-improvement requirements.

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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 10

A healthcare provider uses Azure Video Analyzer for Media to extract insights from surgical videos. They need to ensure that no patient health information (PHI) is stored in the transcriptions. What is the best approach?

Accepted Answer

Enable content moderation in Video Analyzer for Media settings.. Enabling content moderation in Azure Video Analyzer for Media automatically redacts or flags sensitive content, including PHI, from transcriptions during the indexing process. This built-in feature ensures that PHI is not stored in the output without requiring additional post-processing or manual deletion.

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Use Azure AI Content Safety to post-process transcriptions.

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Disable indexing for videos containing PHI.

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Use the 'delete' API to remove all transcripts after processing.

Question 11

You are designing an agentic solution using Azure AI Agent Service with a custom skill that calls an external REST API. The API has rate limits: 100 requests per minute per client. You need to ensure the agent respects this limit without degrading user experience. Which approach should you take?

Accepted Answer

Use a token bucket rate limiter with a shared counter stored in Azure Cache for Redis. A token bucket rate limiter with a shared counter stored in Azure Cache for Redis provides a distributed, atomic mechanism to enforce a strict 100 requests per minute per client limit across multiple instances of the agent. This approach ensures that the rate limit is respected globally without introducing unnecessary delays when the limit is not reached, thus maintaining a responsive user experience.

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Set a fixed delay of 600ms between each API call

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Configure the skill to handle HTTP 429 responses with retry-after logic

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Implement exponential backoff in the skill code

Question 12

A company deploys a custom question answering project in Azure AI Language. Users report that the bot sometimes returns irrelevant answers. The knowledge base contains hundreds of QnA pairs. You need to improve answer relevance without retraining the model. What should you do?

Accepted Answer

Add alternate phrases to existing QnA pairs for common user queries.. Adding alternate phrases to existing QnA pairs directly improves the bot's ability to match user queries to the correct answer without retraining. In Azure AI Language's custom question answering, the model uses a ranker that compares the user's input against the questions and alternate phrases in the knowledge base. By providing more varied phrasings for common queries, you increase the likelihood of a high-confidence match, thereby reducing irrelevant answers.

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Increase the confidence score threshold in the project settings.

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Reduce the number of QnA pairs to decrease ambiguity.

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Enable active learning to let the bot suggest new questions based on user queries.

Question 13

You deploy the ARM template shown in the exhibit to create an Azure AI Search indexer. The indexer fails to run, and you see an error that the skillset 'demo-skillset' does not exist. What is the most likely cause?

Accepted Answer

The skillset resource was not deployed before the indexer. The ARM template deploys resources in parallel by default, so the indexer can be created before the skillset. Since the indexer references 'demo-skillset' in its configuration, the deployment fails because the skillset resource does not exist at the time the indexer is created. To fix this, you must add a 'dependsOn' property in the indexer resource definition to ensure the skillset is deployed first.

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The field mapping source field 'metadata_storage_path' is incorrect

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The schedule start time is in the past, causing the indexer to be disabled

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The data source 'demo-datasource' does not exist

Question 14

Refer to the exhibit. You are reviewing an Azure OpenAI Service API request. The deployment-id is 'gpt-4o'. The user asks 'What is the capital of France?' The response is cut off mid-sentence. Based on the parameters, what is the most likely cause?

Accepted Answer

The stop parameter is causing early termination. The `stop` parameter in an Azure OpenAI API request defines a sequence of tokens that, when generated, causes the model to stop producing further output. If the `stop` sequence appears in the generated text, the response will be truncated at that point, even mid-sentence. In this scenario, the cut-off response is most likely due to the `stop` parameter matching a token in the generated output, not because of token limits or temperature settings.

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The max_tokens value is too low

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The temperature setting is too high

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The system message is missing a context

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.