AI-102 Practice Exam 52 — 20 Questions

Question 1

You are deploying an Azure OpenAI model for a public-facing FAQ assistant. The assistant must answer only questions covered by a fixed set of approved topics, and any off-topic question should receive a polite refusal. Which approach most directly enforces this behavior?

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

Accepted Answer

Configure a system message that defines the allowed topics and instructs the model to refuse anything outside them.. The system message is the model's persistent instruction set, making it the natural place to declare allowed topics and refusal behavior. For a fixed FAQ scope, this directly shapes every response. The other settings influence sampling, safety categories, or capacity, none of which restrict the assistant to approved topics.

Answer

Raise the frequency_penalty parameter to reduce repeated off-topic phrases.

Answer

Deploy the model with the lowest available quota tier to limit how many questions users can ask.

Answer

Set the model deployment's content filter to block the 'violence' category at high severity.

Question 2

You are deploying an Azure AI solution that must process images stored in an Azure Blob Storage account. The solution uses the Computer Vision API and must be able to access the images without exposing storage account keys in code. You need to configure authentication. What should you do?

Accepted Answer

Assign a managed identity to the Azure resource hosting the solution and grant it the Storage Blob Data Reader role on the storage account.. Using a managed identity with the appropriate RBAC role allows the solution to authenticate to Blob Storage without embedding secrets. The Storage Blob Data Reader role grants the necessary read access. This method is secure, requires no credential management, and aligns with Azure best practices for passwordless authentication.

Answer

Store the storage account key in Azure Key Vault and retrieve it at runtime using the application's service principal.

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Use a shared access signature (SAS) token generated with the storage account key and embed it in the application configuration.

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Enable anonymous read access on the blob container and configure the Computer Vision client to use the public URLs of the images.

Question 3

Your team ships a generative assistant that calls a custom function to look up live order status. Users report that the assistant sometimes invents an order status instead of calling the function, and that when it does call the tool the arguments are occasionally malformed. You need to make tool invocation more reliable while keeping the existing model. What should you do?

Accepted Answer

Define the function with a clear name, description, and parameter schema, set tool_choice to require a tool call, and validate returned arguments before executing.. Reliable function calling comes from three things working together: descriptive tool metadata so the model understands when and how to call it, an explicit tool_choice that forces a call when the intent is known, and client-side validation of the returned arguments before execution. Penalties, larger models, and prompt-only pleading do not enforce tool invocation or argument correctness.

Answer

Switch the deployment to a larger model and keep the existing function definitions unchanged.

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Add the phrase "always call the function" to the system message and leave tool_choice at its default value.

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Raise the frequency_penalty so the model is discouraged from repeating previously invented order statuses.

Question 4

You are building an Azure AI Language solution that must extract named entities from support tickets and classify each entity as a person, organization, or location. The tickets are stored as UTF-8 text files. You need to call the REST API for Named Entity Recognition (NER) and ensure the response includes entity categories. Which request should you send?

Accepted Answer

POST to https://<resource>.cognitiveservices.azure.com/language/:analyze-text?api-version=2023-04-01 with a JSON body containing "kind": "EntityRecognition" and the documents array.. The correct request uses the unified Language service analyze-text endpoint with the EntityRecognition task, which returns entities with categories such as Person, Organization, and Location. The older Text Analytics endpoint is deprecated, and other task types like KeyPhraseExtraction do not provide entity categorization. Using POST with a JSON body is mandatory for analyze-text.

Answer

POST to https://<resource>.cognitiveservices.azure.com/text/analytics/v3.1/entities/recognition/general with the documents array.

Answer

POST to https://<resource>.cognitiveservices.azure.com/language/:analyze-text?api-version=2023-04-01 with a JSON body containing "kind": "KeyPhraseExtraction" and the documents array.

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GET to https://<resource>.cognitiveservices.azure.com/language/:analyze-text?api-version=2023-04-01 with the text as a query parameter.

Question 5

You are implementing an Azure AI Search enrichment pipeline that extracts text from PDF documents stored in Azure Blob Storage. The PDFs are scanned images with no embedded text layer. You need to ensure the extracted text is available for downstream skills. Which skill should you add to the skillset?

Accepted Answer

Microsoft.Skills.Vision.OcrSkill. The OCR skill is specifically designed to extract text from images, including scanned PDFs, within an Azure AI Search enrichment pipeline. It leverages the Computer Vision Read API to recognize text and output it for further processing. Other skills like Split, Merge, or custom Web API do not provide built-in OCR capabilities. Therefore, adding the OCR skill is the correct approach to make the scanned PDF content searchable.

Answer

Microsoft.Skills.Text.SplitSkill

Answer

Microsoft.Skills.Text.MergeSkill

Answer

Microsoft.Skills.Custom.WebApiSkill

Question 6

Your Azure AI Language custom question answering project answers product questions from a knowledge base. Users report that the bot returns the same generic answer regardless of how they phrase a question, even when the knowledge base contains the correct information. You need to diagnose why the model is not matching semantically similar user phrasings to the right answer. What should you do first?

Accepted Answer

Review the project's default answer and confirm the deployed model is the one being queried by the bot. When every phrasing produces the same generic response, the runtime is almost certainly falling back to the project's default answer, which occurs when no question-answer pair scores above the threshold or when the bot queries a deployment that lacks the trained model. Inspecting the default answer and verifying the deployment target isolates configuration problems before any content changes are made. Content remedies such as alternate questions or more documents address different symptoms.

Answer

Enable active learning and wait for the model to retrain automatically on user queries

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Add alternate questions to each question-answer pair in the project

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Increase the number of documents in the knowledge base and re-index the project

Question 7

A company is building an agent using Azure AI Foundry Agent Service. The agent must be able to call an external REST API that returns real-time inventory data. The API requires an OAuth 2.0 token that changes frequently. Which approach should the team use to enable the agent to call this API securely?

Accepted Answer

Use a function tool in the agent definition that invokes an Azure Function, which retrieves the token from Azure Key Vault and calls the REST API.. The correct approach is to use a function tool that invokes an Azure Function, which securely retrieves the OAuth token from Azure Key Vault and calls the REST API. This keeps credentials out of the agent and handles token refresh. Other options either expose the token, rely on unsupported authentication, or misuse the knowledge base.

Answer

Configure the agent to use a managed identity and assign it the necessary permissions to call the REST API directly.

Answer

Store the OAuth token in the agent's knowledge base and have the agent retrieve it when needed.

Answer

Embed the OAuth token in the agent's system prompt so the agent can include it in API calls.

Question 8

A team is building an agent with Azure AI Foundry Agent Service that must call several internal function tools. The team reports that the model sometimes invents function names that do not exist and passes arguments that do not match the tool schema. Which TWO practices should the team adopt to reduce these failures? (Choose two.)

Accepted Answer

Handle tool-call validation errors by returning a structured error message to the model and allowing it to retry. Reliable function calling depends on a precise contract and a recovery path. Clear names, descriptions, and JSON schemas tell the model exactly which tools exist and how to call them, while returning structured validation errors lets the model self-correct on a subsequent turn. Temperature, parallel-call settings, and token limits affect other behaviors and do not resolve hallucinated function names or malformed arguments.

Answer

Disable parallel tool calls so the model can only invoke one function per turn

Answer

Set the agent's temperature to a high value so the model explores more tool combinations

Answer

Increase the model's max tokens so it has more room to explain each function call

Question 9

A marketing team uses Azure OpenAI Service to draft product descriptions. They report that outputs vary widely in tone and sometimes ignore the required brand voice. You need to make responses follow a fixed set of style rules consistently across all requests without changing the model deployment. What should you do?

Accepted Answer

Add a system message that defines the brand voice and formatting rules. System messages carry instructions the model treats with high priority and apply to the whole request, so they are the right mechanism for enforcing brand voice, tone, and formatting rules. Sampling parameters such as temperature and presence_penalty alter randomness and repetition, and additional deployments of the same model behave identically, so none of them can encode style policy.

Answer

Create a second deployment of the same model with a different name

Answer

Raise the temperature value to increase output creativity

Answer

Set the presence_penalty parameter to a large positive value

Question 10

You are planning to deploy an Azure AI solution that uses an Azure AI Services multi-service resource. The solution must be deployed across multiple Azure regions to provide high availability. You need to ensure that the solution can fail over automatically if one region becomes unavailable. What should you do?

Accepted Answer

Deploy the Azure AI Services resource in two regions and configure a Traffic Manager profile with priority routing.. Deploying the resource in two regions and using Traffic Manager with priority routing ensures that traffic is directed to the primary region and automatically redirected to the secondary if the primary fails. This provides the required high availability and automatic failover. Other options either replicate data without failover or rely on DNS without health checks.

Answer

Deploy the Azure AI Services resource in one region and configure a custom domain with multiple CNAME records.

Answer

Deploy the Azure AI Services resource in two regions and use Azure Front Door with session affinity enabled.

Answer

Deploy the Azure AI Services resource in one region and enable geo-redundant storage for the resource.

Question 11

You are deploying a generative AI solution by using Azure OpenAI Service. The solution must ground model responses in a private Azure AI Search index that contains product manuals. You need to configure the model deployment so that the service automatically retrieves relevant chunks from the index and includes them in the prompt at inference time. Which configuration should you use?

Accepted Answer

Enable the 'Azure OpenAI On Your Data' feature by specifying the Azure AI Search data source in the model's chat completion request.. Azure OpenAI On Your Data is designed to connect a deployment to an Azure AI Search index so that retrieval and prompt augmentation happen automatically at request time. It handles embedding, chunk ranking, and citation generation, which matches the requirement to ground responses in private manuals without custom orchestration.

Answer

Upload the manuals to the model's training dataset and enable continuous training on the deployment.

Answer

Fine-tune the base model on the contents of the product manuals and deploy the fine-tuned model for inference.

Answer

Create a custom retrieval pipeline by using Azure Functions that queries the index and appends results to each user message before calling the model.

Question 12

A team is prototyping an Azure OpenAI chat application and wants to iterate quickly on prompt wording without changing application code or redeploying. They also want to compare outputs from different prompt variants side by side. Which Azure AI Foundry feature should they use?

Accepted Answer

The prompt flow authoring experience in Azure AI Foundry. The requirement is to iterate on prompts and compare variants without code changes. Prompt flow in Azure AI Foundry is the authoring and orchestration surface designed for exactly that, letting prompts be edited, run, and evaluated visually. Quota pages, key and endpoint blades, and content filter settings are administrative or safety configuration surfaces and provide no prompt editing or comparison capability.

Answer

The Azure OpenAI resource's keys and endpoint blade

Answer

The model deployment quota page in the Azure portal

Answer

The content filter configuration page for the deployment

Question 13

A company uses Azure AI Language's custom named entity recognition (NER) to extract product names from support tickets. They have trained a model with 500 labeled entities across 200 documents. During evaluation, they notice the model has high precision but low recall for a specific product category. What should they do to improve recall for that category?

Accepted Answer

Add more labeled examples of the product category to the training dataset, ensuring they cover different contexts and sentence structures.. Low recall for a specific category means the model is failing to identify many true instances. The most effective solution is to add more labeled examples of that category in diverse contexts, which helps the model learn the patterns. Adjusting thresholds or removing data does not address the underlying data gap.

Answer

Increase the model's confidence threshold for entity extraction to reduce false positives.

Answer

Use the model's evaluation metrics to identify and remove documents that contain ambiguous entity mentions.

Answer

Retrain the model with a higher learning rate to help it converge faster on the underrepresented category.

Question 14

You are building an Azure AI Search knowledge mining pipeline that enriches scanned PDF invoices. The PDFs are stored in Azure Blob Storage, and you need to extract text from each page before running downstream entity recognition. The solution must minimize development effort and rely on a built-in cognitive skill. Which skill should you add to the skillset?

Accepted Answer

OcrSkill. The pipeline must convert image-based invoice content into text before any language skill can run. OcrSkill is the built-in Azure AI Search cognitive skill designed for that conversion and outputs a text field that downstream skills consume. Image analysis, entity recognition, and key phrase extraction all assume text already exists, so they cannot satisfy the extraction requirement on their own.

Answer

EntityRecognitionSkill

Answer

ImageAnalysisSkill

Answer

KeyPhraseExtractionSkill

Question 15

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 16

You are using Azure AI Search to index a set of contracts. You need to extract named entities such as organizations, people, and dates from the contract text and store them as separate fields in the index. Which skill should you add to the skillset?

Accepted Answer

Entity Recognition skill. Entity Recognition is the built-in Azure AI Search skill that calls Azure AI Language to detect named entities with type and subtype metadata. Its output includes categorized entities such as organizations, people, and dates, which can be projected into separate index fields. The other language skills perform different functions and do not produce typed entity output.

Answer

Text Translation skill

Answer

Language Detection skill

Answer

Key Phrases skill

Question 17

A bank is deploying an Azure AI Foundry agent that provides account balance information over the phone. The agent must authenticate callers by using voice biometrics before revealing any account details. The bank wants to integrate this authentication step into the agent's conversation flow without exposing sensitive data to the language model. Which approach should the bank use?

Accepted Answer

Add a voice biometrics tool to the agent and configure a pre-action that runs before any account-related tool call.. The bank needs voice biometric authentication integrated into the agent flow without exposing sensitive data to the language model. A voice biometrics tool with a pre-action that gates account-related tool calls achieves this: the biometric check happens outside the model, and only a success token is passed to the orchestration layer, allowing account tools to run. This satisfies security and integration requirements.

Answer

Include the caller's voiceprint in the system message so the model can compare it to the live audio.

Answer

Use Azure Communication Services to record the call and send the audio to the agent as a text transcript for authentication.

Answer

Configure the agent to ask for the caller's account number and PIN, then validate them against a database before answering.

Question 18

A company uses Azure AI Search to index documents from an Azure SQL Database. They need to ensure that deleted rows in the database are also removed from the search index during incremental indexing. They have configured the data source with change detection policies. What should they do to enable deletion detection?

Accepted Answer

Add a soft delete column to the table that indicates when a row is deleted, and configure the data source with a soft deletion policy that references that column.. To enable deletion detection for Azure SQL Database in Azure AI Search, you must implement a soft delete column in the table and configure the data source with a soft deletion policy that references that column. The indexer then uses this column to identify and remove deleted documents from the index during incremental indexing.

Answer

Configure the indexer to use the high water mark change detection policy and set the deletion detection policy to 'none'.

Answer

Create a SQL trigger that calls the Azure AI Search REST API to delete the document when a row is deleted.

Answer

Use a view that filters out deleted rows and configure the indexer to use that view as the data source.

Question 19

You are building a conversational language understanding (CLU) project in Azure AI Language. You need to ensure the model can correctly interpret user utterances that include both an intent and multiple entities. Which two actions should you perform? (Choose two.)

Accepted Answer

Add utterances that contain multiple entities and label each entity with the correct entity type.. To handle utterances with multiple entities, you need labeled examples that include multiple entities with correct types, and you must train and evaluate the model to verify performance. Creating intents per entity, looking for a non-existent setting, or using prebuilt entities for custom types will not achieve the goal.

Answer

Add a prebuilt entity component for each custom entity to supplement training data.

Answer

Enable the "Extract multiple entities" option in the project settings.

Answer

Define a separate intent for each entity type to improve extraction accuracy.

Question 20

Your Azure AI Agent Service agent occasionally calls a 'createOrder' tool with plausible but incorrect item codes. You must ensure that the order is created only when the item code exists in the product database, and that invalid calls are rejected before any order is written. Which approach should you take?

Accepted Answer

Validate the item code inside the function tool implementation and return an error result to the agent when the code is unknown.. Validation belongs in the tool implementation, where it runs deterministically before any write occurs. The model may still propose a bad code, but the tool rejects it and returns an error the agent can act on, guaranteeing that only codes present in the product database result in orders.

Answer

Add the full product database to the agent's instructions so the model can check codes itself before calling the tool.

Answer

Enable content filtering on the model deployment and add a blocklist entry for malformed item codes.

Answer

Increase the model temperature to zero and add a system message telling the model to verify item codes before calling the tool.