AI-102 Practice Test 33 — 15 Questions

Question 1

You are building a generative AI solution using Azure OpenAI Service that must generate code snippets. The solution must ensure that the generated code does not include any known vulnerable patterns, such as SQL injection. You need to implement a safeguard. What should you do?

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

Accepted Answer

Implement a post-processing step that analyzes the generated code with a static analysis tool and rejects or sanitizes unsafe snippets.. The most reliable safeguard is to post-process the generated code with a static analysis tool that detects vulnerable patterns like SQL injection. This provides deterministic enforcement, rejecting or sanitizing unsafe outputs before use. While prompt instructions and fine-tuning can help, they do not guarantee security. Static analysis is a standard, effective method for ensuring code safety.

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Add a system message instructing the model to never generate vulnerable code.

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Fine-tune the model on a dataset of secure code to reduce the likelihood of generating vulnerable patterns.

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Use Azure AI Content Safety to scan the generated code for vulnerabilities and block unsafe outputs.

Question 2

A company is building a knowledge mining solution using Azure AI Search. They need to extract text from handwritten notes stored as images in Azure Blob Storage. They want to use the built-in OCR skill in a skillset. Which cognitive service does the OCR skill rely on?

Accepted Answer

Azure AI Vision. The built-in OCR skill in Azure AI Search is powered by the Computer Vision OCR API, which is part of Azure AI Vision. This service extracts text from images, including handwritten text, and returns it as structured data. The skill then makes this text available for indexing. Other cognitive services like Language, Document Intelligence, and Translator serve different purposes and are not used by the OCR skill.

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Azure AI Language

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Azure AI Document Intelligence

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Azure AI Translator

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.

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Set presence_penalty to 0.5.

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Set max_tokens to 2000.

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Set frequency_penalty to 0.7.

Question 4

A company is deploying an agent built with Azure AI Foundry Agent Service to production. The agent must log all interactions for auditing and compliance. The team needs to capture the full conversation history, including tool calls and their results. Which approach should they use?

Accepted Answer

Enable diagnostic settings on the Azure AI Foundry resource to send logs to Azure Monitor and Log Analytics.. Enabling diagnostic settings to send logs to Azure Monitor and Log Analytics is the correct approach because it provides a centralized, durable, and queryable audit trail of all agent interactions, including tool calls. Other methods are either local, manual, or incomplete, and do not meet compliance needs for production auditing.

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Use the agent's built-in conversation history feature and periodically export it manually via the portal.

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Configure the agent to write conversation logs to a local file on the client application.

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Instruct the agent to include a summary of each interaction in its response for the user to save.

Question 5

A media company is building a knowledge mining solution with Azure AI Search. They need to enrich video assets by extracting spoken words from the audio track and then indexing that transcript for search. Which two components must be included in the enrichment pipeline? (Choose two.)

Accepted Answer

A custom skill that calls Azure AI Video Indexer or the Speech service to transcribe the audio.. To index spoken words from video, the pipeline needs a data source and indexer to pull video assets from Blob Storage, plus a custom skill that calls a speech-to-text service because Azure AI Search has no built-in audio transcription skill. OCR, synonym maps, and scoring profiles operate on images, queries, or ranking respectively and cannot produce a transcript.

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A scoring profile that boosts video documents by duration.

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A synonym map that maps spoken words to their text equivalents.

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A skillset entry that calls the built-in OCR skill on the video file.

Question 6

A retail company is creating an Azure AI Foundry agent that helps customers find products. The agent must be able to call a product search API and a store inventory API. The team wants to define these capabilities so the agent can invoke them when needed. What should the team do?

Accepted Answer

Add the two APIs as tools in the agent definition, providing their OpenAPI schemas.. The team needs to enable the agent to call two external APIs. In Azure AI Foundry, tools are the mechanism for integrating external services. By adding each API as a tool with its OpenAPI schema, the agent gains the ability to invoke them with correct parameters and handle responses. This is the native, supported approach and requires minimal custom code.

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Embed the API endpoints in the agent's system message and instruct the model to call them by using HTTP requests.

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Use the agent's built-in code interpreter to write Python code that calls the APIs.

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Create an Azure Function for each API and configure the agent to use them as skills.

Question 7

You are planning to deploy an Azure AI solution that uses multiple Azure AI services. You need to monitor the solution for performance and operational issues. You want to collect metrics and logs from all the services and analyze them in a central location. What should you use?

Accepted Answer

Azure Monitor. Azure Monitor is the correct choice because it is the comprehensive monitoring solution for collecting and analyzing metrics and logs from Azure resources, including AI services. It enables centralized monitoring, alerting, and diagnostics, which are essential for maintaining the performance of a multi-service AI solution.

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Azure Advisor

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Azure Service Health

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Azure Cost Management

Question 8

A company uses Azure Document Intelligence to process custom forms. They have trained a custom model using labeled data. They need to improve the model's accuracy for a specific field that is frequently misrecognized. The field appears in a consistent location but has varying formats. Which two actions should they take? (Choose two.)

Accepted Answer

Add more labeled samples that include variations of the field's format to the training dataset.. Improving a custom model's accuracy requires enhancing the training data. Adding labeled samples with format variations exposes the model to diverse representations, while correcting low-confidence instances ensures the training set is accurate. These actions directly address the model's learning process, unlike threshold adjustments or architectural changes.

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Use the prebuilt model for that field type instead of the custom model.

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Retrain the model using the same dataset but with a different neural network architecture.

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Increase the model's confidence threshold to reduce false positives for that field.

Question 9

You are deploying an Azure AI solution that uses Azure AI Search. The solution must be able to access the search service from an Azure virtual network without traversing the public internet. You need to configure the search service to meet this requirement. What should you do?

Accepted Answer

Create a private endpoint for the search service and configure the virtual network to use it.. To access Azure AI Search privately from a virtual network, you must use a private endpoint, which assigns a private IP address from your subnet to the search service. This ensures all traffic remains within the Azure backbone. Other options either use public internet or do not provide a private IP.

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Configure the search service to use an IP firewall and add the virtual network's public IP address.

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Deploy the search service with a public endpoint and use Azure Front Door to route traffic privately.

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Enable a service endpoint for Azure AI Search on the virtual network subnet.

Question 10

You are building an Azure AI solution that uses Azure AI Vision to analyze images. The solution must be able to extract text from images and return the text in a structured format. You need to choose the appropriate Azure AI Vision feature. What should you use?

Accepted Answer

Read API. The Read API is the OCR component of Azure AI Vision. It extracts text from images and returns it in a structured JSON format with lines and words. This is the correct choice for extracting text from images in a structured format.

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Image Analysis

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Face API

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Custom Vision

Question 11

You are creating an Azure AI Search index for a knowledge mining solution. The index must support searching for documents by a required category field that can have one of five predefined values, and you want to enable faceted navigation on that field. Which index field configuration should you use?

Accepted Answer

Edm.String with filterable and facetable set to true, and searchable set to false.. A category field with predefined values is best modeled as a filterable and facetable string field that is not searchable, because full-text analysis is unnecessary and can interfere with exact matching. Making it searchable could tokenize values, and omitting facetable would break faceted navigation. The retrievable attribute should remain true if the value needs to be displayed.

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Edm.String with searchable set to true and filterable set to false.

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Edm.String with retrievable set to false and filterable set to true.

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Edm.Int32 with filterable set to true and facetable set to false.

Question 12

You are deploying an Azure AI solution that uses Azure OpenAI Service and Azure AI Language. The solution must ensure that each service has its own managed identity and that access to keys is restricted. You need to configure authentication for the services. What should you do?

Accepted Answer

Enable managed identities for each Azure AI service and grant them access to Azure Key Vault.. Managed identities provide an identity for the service in Microsoft Entra ID, allowing it to authenticate to resources like Key Vault without storing credentials. Granting each service's managed identity access to Key Vault ensures secure retrieval of secrets and aligns with the principle of least privilege. Other options either rely on shared secrets or do not provide per-service identities.

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Configure the services to use API keys stored in Azure App Configuration and enable Azure AD authentication for the application.

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Use Azure AD service principals with client secrets for each service and store the secrets in Azure Key Vault.

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Store the service keys in Azure Key Vault and configure the application to retrieve them at runtime using the Azure SDK.

Question 13

You are building an Azure AI solution that uses Azure AI Language to analyze customer feedback stored in an Azure Blob Storage container. The container contains 500,000 small text documents. You need to minimize the total time required to analyze all documents and minimize the number of API calls. What should you do?

Accepted Answer

Use the Analyze Text API asynchronously by submitting a batch job that references the documents in Azure Blob Storage.. For large volumes of documents already stored in Azure Blob Storage, the asynchronous Analyze Text API is the intended mechanism. It accepts a batch job that references the storage container, processes documents in parallel, and returns results for retrieval. This approach reduces the number of API calls to a few (submit and poll) and shortens overall processing time compared with synchronous per-document calls.

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Submit a separate synchronous request for each document by using the Analyze Text API.

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Use Azure AI Document Intelligence to extract text from each document and then call the Azure AI Language API for each extracted document.

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Combine all documents into a single request by using the Analyze Text API with a custom text analytics task.

Question 14

You are deploying an Azure AI solution that uses Azure Cognitive Services. You need to ensure that the API keys are stored securely and can be rotated automatically. What should you use?

Accepted Answer

Azure Key Vault. Azure Key Vault is the correct choice because it provides secure storage for secrets and supports automated rotation. It integrates with other Azure services and allows you to manage keys centrally. The other options do not offer the same level of security and rotation capabilities.

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Azure Storage account with SAS tokens

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Environment variables in Azure App Service

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Azure App Configuration

Question 15

You are designing an Azure AI solution that uses multiple Azure AI services, including Azure AI Vision and Azure AI Language. You need to ensure that the solution can be deployed in a way that minimizes latency between services and provides a single endpoint for management. What should you use?

Accepted Answer

Deploy a single Azure AI Services multi-service resource that includes both Vision and Language capabilities.. A multi-service Azure AI Services resource bundles multiple AI capabilities into a single Azure resource with one endpoint and key. Deploying it in one region ensures all services are co-located, reducing latency. It also simplifies management by consolidating billing and access control, meeting both requirements.

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Deploy a single Azure AI Services resource for each service and use private endpoints to connect them.

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Deploy each service as a separate resource in the same region and use Azure API Management to aggregate them.

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Deploy each service as a separate resource in different regions and use Azure Front Door to route requests.