AI-102 Practice Test 46 — 15 Questions

Question 1

A developer needs to generate a descriptive caption for an image using Azure AI Vision. The image contains a dog catching a frisbee in a park. Which feature of Image Analysis should they use?

Accepted Answer

Caption. The caption feature of Azure AI Vision Image Analysis generates a natural language description of an image's content. It is the only feature that produces a sentence like 'a dog catching a frisbee in a park'. Tags, objects, and read serve different purposes: tags provide keywords, objects detect and locate items, and read extracts text. Therefore, caption is the correct choice.

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Read

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Objects

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Tags

Question 2

You are managing an Azure AI solution that uses Azure OpenAI. You need to ensure that the solution can only be accessed by applications hosted in a specific Azure Virtual Network. The solution must not be accessible from the public internet. What should you configure?

Accepted Answer

Azure OpenAI private endpoint and disable public network access.. To restrict Azure OpenAI access to only a specific virtual network and prevent public internet access, you should configure a private endpoint for the Azure OpenAI resource and disable public network access. This ensures that the resource is only accessible via private IP addresses within the virtual network, and all public access is blocked, satisfying the strict isolation requirement.

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Azure OpenAI managed identity with role-based access control (RBAC).

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Azure OpenAI network security group (NSG) on the resource's subnet.

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Azure OpenAI firewall with IP restrictions for the virtual network's NAT gateway.

Question 3

A security firm wants to analyze live video from cameras at a warehouse gate to count the number of people entering and leaving. They require a ready-to-use service that provides a real-time count and does not require training a custom model. Which Azure AI service should they use?

Accepted Answer

Azure AI Vision Spatial Analysis. The security firm needs a ready-to-use service for real-time people counting from live video. Azure AI Vision Spatial Analysis is purpose-built for this scenario, offering pre-trained models that analyze RTSP streams and count people crossing lines or zones without custom training. Other services either focus on static images or require significant custom development, making them unsuitable for immediate deployment.

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Azure AI Custom Vision

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Azure AI Face

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Azure Video Indexer

Question 4

You are deploying an Azure AI solution that uses multiple Cognitive Services resources. You need to ensure that the solution can be deployed to multiple regions and that each region has its own endpoint and key. What should you use to manage the deployment?

Accepted Answer

Azure Resource Manager (ARM) templates with parameters for region-specific values.. ARM templates are the recommended infrastructure-as-code tool for deploying Azure resources consistently across multiple regions. By using parameters, you can customize region-specific settings such as location, endpoint names, and SKUs. This approach ensures repeatability and simplifies management of multi-region deployments.

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A single Cognitive Services resource with multiple endpoints configured in the application code.

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Azure Policy definitions that enforce the creation of Cognitive Services resources in specific regions.

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Azure CLI scripts that create resources in each region sequentially.

Question 5

You are designing an Azure AI solution that uses an Azure AI services multi-service account. The solution must call the service from an Azure Kubernetes Service (AKS) cluster without embedding account keys in application code. You need to configure authentication and authorization so that the workload identity used by the pods can be granted access. (Choose two.)

Accepted Answer

Assign the Cognitive Services User role to the managed identity.. Keyless access from AKS pods requires two things: the cluster must federate the pod identity with Microsoft Entra ID, and that identity must be granted a role on the Azure AI services resource. Workload identity federation provides the token acquisition mechanism, while the Cognitive Services User role provides the authorization. Together they let the application call the service without storing or handling account keys.

Answer

Store the account key in an Azure Key Vault secret and reference it from the pod.

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Create a service principal with a client secret and mount the secret into the pod.

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Enable local authentication on the Azure AI services account.

Question 6

A healthcare company uses Azure AI Language's custom question answering to build a bot that answers patient FAQs. The knowledge base contains documents with sensitive information. The company needs to ensure that the bot only returns answers from documents that the user is authorized to access. What should they implement?

Accepted Answer

Implement document-level access control by using metadata tags and filtering in the query.. The correct solution is to use metadata tags on documents and apply filters during query execution. This allows the custom question answering service to return only answers from documents that match the user's authorization level. Other options either address resource management, require complex duplication, or only authenticate without enforcing access control.

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Configure the bot to prompt users for credentials before answering and validate against Azure Active Directory.

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Enable role-based access control (RBAC) on the Azure AI Language resource.

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Use separate knowledge bases for each user group and route queries based on user identity.

Question 7

You are deploying an Azure AI Language resource that will process customer feedback. The resource must be accessible only from a specific Azure virtual network and must allow access from an on-premises network via a site-to-site VPN. You need to configure network security for the resource. What should you do?

Accepted Answer

Configure a private endpoint for the Azure AI Language resource and disable public access.. A private endpoint is the correct solution because it assigns a private IP address from your virtual network to the Azure AI Language resource, allowing secure access from both the VNet and on-premises via VPN. Disabling public access ensures the resource is not reachable from the internet. This configuration satisfies the network isolation requirements.

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Use Azure Private Link to create a private endpoint for the Azure AI Language resource and enable public access for on-premises clients.

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Enable public access and configure IP firewall rules to allow only the on-premises public IP address.

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Configure a service endpoint for Azure AI Language on the virtual network subnet and enable public access for on-premises clients.

Question 8

You are planning a new Azure AI solution that will use several Azure AI services, including Azure AI Vision and Azure AI Language. The solution must allow developers to authenticate by using their Microsoft Entra ID credentials without storing service keys in code. You need to configure the Azure AI services resource to support this authentication method. What should you do?

Accepted Answer

Configure the Azure AI services resource to disable local authentication and grant developers the Cognitive Services User role on the resource.. To allow developers to authenticate with Microsoft Entra ID, you must disable key-based authentication on the Azure AI services resource and assign the appropriate role, such as Cognitive Services User, to the developers. This ensures that access is granted through Entra ID tokens rather than shared keys, aligning with security best practices.

Answer

Enable local authentication on the Azure AI services resource and distribute the keys to developers.

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Store the Azure AI services keys in Azure Key Vault and provide developers access to the vault.

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Create a managed identity for the Azure AI services resource and assign it the Cognitive Services User role.

Question 9

You are developing a generative AI solution using Azure OpenAI Service. The solution must generate product descriptions based on a set of attributes provided by the user. You need to ensure the output adheres to a specific JSON schema. Which feature should you use to enforce the structure of the model's response?

Accepted Answer

Structured outputs with a JSON schema definition.. Structured outputs in Azure OpenAI Service enable you to specify a JSON schema that the model's response must conform to. This guarantees that the generated output is valid JSON and includes all required fields, which is essential for generating product descriptions with a consistent format. It is a robust way to enforce structure without relying on prompt engineering alone.

Answer

Function calling with a defined function that returns JSON.

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Temperature set to 0 to make output deterministic.

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Prompt engineering with few-shot examples of JSON.

Question 10

A team is using Azure OpenAI Service to summarize long legal contracts. They observe that summaries sometimes miss clauses located near the end of the document. The contracts are far longer than the model's maximum context window. What should they implement to improve coverage of the entire document?

Accepted Answer

Split the contract into overlapping chunks, summarize each chunk, then combine the partial summaries into a final summary.. Documents longer than the context window must be processed in pieces. Overlapping chunking ensures clauses spanning boundaries survive, and a combine step merges per-chunk summaries into a coherent whole. Sampling parameters and output length settings operate on generation behavior and cannot extend how much source text the model receives.

Answer

Lower the max_tokens parameter to force the model to read further into the document.

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Increase the temperature parameter so the model explores more of the contract content.

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Set the top_p parameter to 1.0 and rely on nucleus sampling to expand context coverage.

Question 11

You are deploying a generative AI solution that uses Azure OpenAI Service with your own data stored in Azure AI Search. Users report that answers are sometimes pulled from documents the user is not permitted to see, because the retrieval step searches the entire index. You need to ensure each user only receives answers grounded in documents they are authorized to access, without creating a separate index per user. What should you do?

Accepted Answer

Add a security filter field to the Azure AI Search index and pass the user's group or tenant identifier as an OData filter on each query.. Authorization must be enforced where the documents are selected, not where the model generates text. Adding a filterable access-control field to the Azure AI Search index and applying the caller's identity as an OData filter causes the retrieval step to return only permitted chunks, so the model can never ground an answer in a restricted document. This preserves one shared index while honoring per-user permissions.

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Enable Azure OpenAI content filtering at High severity for both prompts and completions.

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Create a separate Azure OpenAI deployment for each department and route users to the deployment matching their department.

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Increase the chunk size of the indexed documents so that fewer documents are returned per query.

Question 12

You operate a RAG assistant on Azure OpenAI Service that answers questions over a product catalog. The index contains thousands of items, and users complain that answers mix details from unrelated products. You need retrieval to consider semantic meaning and handle paraphrased queries while returning only the most relevant items. What should you implement?

Accepted Answer

Vector search using embeddings generated by an Azure OpenAI embedding model. Embedding-based vector search maps both the query and the catalog items into a semantic space, so similarity reflects meaning rather than exact wording. Paraphrased questions then retrieve the right products, and ranking by vector distance keeps unrelated items out of the context. Keyword, facet, and scoring-profile approaches remain term- or attribute-driven and cannot perform semantic matching.

Answer

Faceted navigation that filters results by product category

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Scoring profiles that boost documents containing a specific field value

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Keyword search using the Azure AI Search simple query syntax

Question 13

You are deploying an Azure AI solution that uses multiple Azure AI services. You need to monitor the solution and receive alerts when the service quota for transactions per second (TPS) is exceeded. What should you use?

Accepted Answer

Azure Monitor metrics and alerts. Azure Monitor metrics and alerts allow you to track the transaction rate and configure alerts when thresholds are breached. By using metrics such as TotalCalls or Throttling, you can receive notifications when TPS limits are exceeded, enabling timely action.

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Azure Service Health

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Azure Advisor

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Azure Cost Management

Question 14

An agent built with Azure AI Foundry Agent Service performs a long-running operation by calling a function tool that starts a batch job. The batch job can take up to 30 minutes, and the agent currently times out because the function tool waits for completion. The team wants the agent to continue the conversation after the job finishes. Which pattern should the team implement?

Accepted Answer

Have the function tool return immediately with a job identifier, then resume the agent when the job completes via a new run or thread message. Long-running work should be decoupled from the agent run. Returning a job identifier immediately keeps the tool call fast, and an external completion event can then deliver the result back to the agent by starting a new run or posting to the thread. Blocking the tool call, moving the job into code interpreter, or enabling parallel calls all fail to provide a reliable continuation path after the job finishes.

Answer

Increase the function tool's HTTP client timeout to 30 minutes so the call waits for the job

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Move the batch job into the code interpreter tool so it runs inside the agent's session

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Enable parallel tool calls so the agent can start the job and keep responding at the same time

Question 15

You are building a generative AI application with Azure OpenAI Service. The application must generate responses that are grounded in a specific set of internal documents stored in an Azure AI Search index. You want to use the built-in 'On Your Data' feature. Which configuration parameter should you set to ensure the model retrieves relevant documents before generating a response?

Accepted Answer

Set the data source type to Azure Cognitive Search and provide the index name.. The On Your Data feature in Azure OpenAI Service allows the model to retrieve relevant documents from a specified data source, such as Azure Cognitive Search, before generating a response. By setting the data source type to Azure Cognitive Search and providing the index name, the model can ground its answers in the internal documents. This is the intended method for retrieval-augmented generation with Azure OpenAI.

Answer

Enable the 'temperature' parameter to a low value to make responses more factual.

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Set the model deployment to a fine-tuned model trained on the documents.

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Use the 'max_tokens' parameter to limit the response length to match document snippets.