AI-200 Practice Test 31 — 15 Questions

Question 1

You are configuring vector search in Azure AI Search for a knowledge mining solution. You need to ensure that the vector search queries return results efficiently and accurately. Which TWO components must be defined in the index schema? (Choose two.)

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

Accepted Answer

A vector search profile that specifies the algorithm and parameters.. To enable vector search in Azure AI Search, the index must include a vector field that stores the embeddings with the correct dimensions, and a vector search profile that defines the algorithm and parameters. These two components are essential for indexing vectors and executing similarity queries. Other features like semantic ranking or suggesters are optional and not required for basic vector search.

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A suggester for autocomplete functionality.

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A scoring profile to boost certain fields.

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A semantic configuration for ranking results.

Question 2

You are deploying a containerized AI application to Azure Container Instances (ACI). The application needs to pull the container image from a private Azure Container Registry (ACR). You want to authenticate ACI to ACR without embedding credentials in the deployment configuration. What should you do?

Accepted Answer

Enable the ACI system-assigned managed identity and grant it the AcrPull role on the ACR.. To authenticate ACI to ACR without embedding credentials, you should use a managed identity. Enabling a system-assigned managed identity on the container group and granting it the AcrPull role on the ACR allows ACI to pull images securely. This eliminates the need to store credentials in the deployment configuration and follows the principle of least privilege.

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Store the ACR credentials in Azure Key Vault and reference them in the ACI deployment using a secret volume.

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Use the ACR admin account username and password in the ACI deployment YAML.

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Configure ACI to use a public container image instead, avoiding the need for authentication.

Question 3

You are deploying a containerized AI model to Azure Container Apps. The model requires a GPU for inference. You configure the container app to use a workload profile with GPU support. However, when the app starts, it fails with an error indicating that the GPU is not available. You need to ensure the container can access the GPU. What should you do?

Accepted Answer

Use a workload profile that includes GPU resources and ensure the container app is configured to use that profile.. Azure Container Apps provides GPU support via specialized workload profiles. To enable GPU access, you must select a workload profile that includes GPU resources and assign it to the container app. This ensures the platform schedules the container on a node with a GPU. Other approaches like environment variables or hostPath mounts do not provision GPU hardware in this managed environment.

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Set the environment variable `NVIDIA_VISIBLE_DEVICES=all` in the container app configuration.

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Deploy the container to Azure Kubernetes Service (AKS) instead, because Azure Container Apps does not support GPU workloads.

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Mount a hostPath volume from the underlying node to the container to expose the GPU device.

Question 4

You are implementing an AI model training pipeline that uses Azure Event Hubs to ingest training data. The pipeline must process data in order per partition and maintain high throughput. You are using the EventProcessorClient in a .NET application. You notice that some partitions are lagging while others are idle. You need to optimize the processor to balance the load across partitions. What should you do?

Accepted Answer

Ensure that the number of EventProcessorClient instances matches the number of partitions and that they are using the same consumer group.. EventProcessorClient instances in the same consumer group use partition ownership to distribute partitions among themselves. For optimal load balancing, the number of instances should equal the number of partitions. If there are fewer instances, some instances handle multiple partitions and may lag. If more, some instances are idle. Ensuring the instance count matches the partition count and that all instances use the same consumer group allows the processor to evenly distribute partitions.

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Configure the EventProcessorClient to use a higher prefetch count.

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Increase the number of partitions in the Event Hub.

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Use a different consumer group for each partition to isolate processing.

Question 5

You are building a RAG solution on Azure AI Search. Documents and their embeddings are updated frequently, and you need the vector index to reflect changes within seconds without rebuilding the entire index. Which indexer behavior should you configure?

Accepted Answer

Enable incremental enrichment and run the indexer on a schedule with change detection policies.. Incremental enrichment combined with change detection policies allows the indexer to identify and process only modified or deleted documents, updating the vector index efficiently. Scheduling the indexer ensures periodic synchronization without full rebuilds. This is the recommended approach for RAG pipelines that need fresh embeddings with minimal latency and resource consumption.

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Use a custom skill that writes embeddings directly to the index bypassing the indexer pipeline.

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Set the indexer to run once manually after each document update via the Azure portal.

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Recreate the vector index and re-upload all embeddings whenever a document changes.

Question 6

You are deploying an AI-powered document processing solution that uses Azure Functions to call Azure OpenAI. The Azure Functions app runs in a Consumption plan. You need to ensure that the function can authenticate to Azure OpenAI without storing any secrets in code or configuration. You want to use a managed identity. What should you do first?

Accepted Answer

Enable a system-assigned managed identity on the Azure Functions app and grant it the Cognitive Services OpenAI User role on the Azure OpenAI resource.. A system-assigned managed identity on the Azure Functions app allows it to authenticate to Azure OpenAI without any secrets. Granting the Cognitive Services OpenAI User role provides the necessary permissions. This is the recommended approach for secretless authentication in Azure.

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Store the Azure OpenAI API key in Azure Key Vault and reference it from the function app settings using a Key Vault reference.

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Create an application registration in Microsoft Entra ID and use its client secret to authenticate the function app to Azure OpenAI.

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Generate a shared access signature (SAS) token for the Azure OpenAI resource and pass it in the request header from the function.

Question 7

You are monitoring an Azure Machine Learning online endpoint that serves a real-time inference model. You need to detect when the model's input data distribution drifts from the training data and trigger an alert. Which Azure Machine Learning feature should you use?

Accepted Answer

Azure Machine Learning data drift detection with a monitor on the deployed model.. Azure Machine Learning data drift detection allows you to monitor a deployed model's input data against a baseline and set up alerts when drift exceeds a threshold. This is the built-in feature for detecting changes in data distribution, making it the correct choice for the scenario.

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Azure Machine Learning responsible AI dashboard with error analysis.

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Azure Monitor Application Insights with custom telemetry for input data.

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Azure Machine Learning model performance monitoring using the model's accuracy metric.

Question 8

You are managing an Azure Cosmos DB for NoSQL account that stores vector embeddings. You need to query the database using the VectorDistance function to find the top 10 most similar items to a given vector. The container has a vector index configured. Which query syntax should you use?

Accepted Answer

SELECT TOP 10 * FROM c ORDER BY VectorDistance(c.embedding, @queryVector). To retrieve the top N most similar items using vector search in Cosmos DB for NoSQL, you must use the ORDER BY clause with the VectorDistance function and limit the results with TOP. This allows the query engine to use the vector index to efficiently find the nearest neighbors. The other options either lack ordering, use invalid syntax, or do not limit results correctly.

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SELECT * FROM c WHERE VectorDistance(c.embedding, @queryVector) > 0.8

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SELECT TOP 10 * FROM c WHERE VectorDistance(c.embedding, @queryVector) ORDER BY c.id

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SELECT * FROM c ORDER BY VectorDistance(c.embedding, @queryVector) OFFSET 0 LIMIT 10

Question 9

You are implementing vector search in Azure AI Search for a document retrieval system. You need to ensure that the vector search returns the most relevant results for a query embedding. Which similarity metric should you configure in the vector search profile?

Accepted Answer

Cosine similarity. Cosine similarity is the most suitable metric for text embeddings because it normalizes for vector magnitude and focuses on semantic orientation. Azure AI Search supports cosine as a vector similarity metric, and it aligns with common embedding models, ensuring that the most semantically similar documents are retrieved based on the query embedding.

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Euclidean distance

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Dot product

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Jaccard similarity

Question 10

Your team runs an AI content-moderation service. When a user submits content, an Azure Function publishes an event to an Azure Event Grid custom topic. A webhook subscription delivers the event to a partner's HTTPS endpoint. The partner reports that they sometimes receive the same event multiple times and asks how to handle this. You need to advise the partner on the correct expectation for Event Grid delivery. What should you tell them?

Accepted Answer

Event Grid provides at-least-once delivery, so the partner should make their endpoint idempotent using the event ID.. Event Grid uses at-least-once delivery, so subscribers may receive the same event more than once, particularly during retries. The event's id property uniquely identifies each event, and subscribers should use it to implement idempotent processing. Claiming exactly-once or at-most-once semantics misrepresents the service and would lead the partner to mishandle duplicates or ignore potential event loss.

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Event Grid provides exactly-once delivery only when the subscriber returns HTTP 200, so duplicates mean the endpoint is returning errors.

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Event Grid provides at-most-once delivery, so missing events are expected and duplicates are impossible.

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Event Grid guarantees exactly-once delivery, so any duplicates indicate a misconfigured subscription filter.

Question 11

You are developing a real-time AI sentiment analysis pipeline using Azure Event Hubs. The pipeline ingests customer feedback messages at a high rate. You need to ensure that each message is processed exactly once by the downstream analytics service, even if the service experiences transient failures. The analytics service uses the EventProcessorClient in the Azure.Messaging.EventHubs library. Which approach should you implement?

Accepted Answer

Implement idempotent processing logic in the downstream service and use checkpointing to minimize reprocessing.. Azure Event Hubs provides at-least-once delivery semantics. To achieve exactly-once processing end-to-end, the consumer must be idempotent. Checkpointing helps reduce duplicate processing but cannot eliminate it entirely because a failure can occur after processing but before checkpointing. Therefore, combining idempotent logic with checkpointing is the correct strategy to meet the requirement.

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Enable the 'EnableCheckpointing' property on the EventProcessorClient and store checkpoints in an Azure Storage account.

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Use the EventProcessorClient with a ProcessEventAsync handler that performs processing and then updates the checkpoint in the same transaction.

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Set the 'MaximumWaitTime' property to zero to ensure immediate checkpointing after each event.

Question 12

You are deploying a containerized AI inference API to Azure Container Apps. The container must read model files from an Azure Files share and write temporary logs to the same share. You need to configure the Container App so the share is available at a specific mount path inside the container. What should you do?

Accepted Answer

Create an Azure Files storage definition in the Container Apps environment and add a volume mount to the Container App revision.. To make an Azure Files share available to a Container App, you must first define the storage at the Container Apps environment level, then reference it as a volume and specify a mount path in the container app revision. This enables the AI workload to persist model files and logs. Other options either refer to non-existent features or confuse container-local volumes with external Azure Files mounts.

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Configure an Azure File share as a bind mount by adding it to the Dockerfile VOLUME instruction.

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Use the Azure CLI command az containerapp storage add to mount the share into the running container instance.

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Attach the Azure Files share directly to the Container App using the Azure portal's 'Networking' blade.

Question 13

You are running a containerized AI inference service on Azure Kubernetes Service (AKS). The service uses a large language model and requires 24 GB of GPU memory. You have a node pool with NVIDIA V100 GPUs (16 GB each). You need to ensure the pod can access sufficient GPU memory. What should you do?

Accepted Answer

Use a node pool with GPUs that have at least 24 GB of memory, such as NVIDIA A100 (40 GB) or V100 (32 GB), and request one GPU.. The inference service requires 24 GB of GPU memory. The existing V100 GPUs have only 16 GB, so a single GPU is insufficient. The correct approach is to use a node pool with GPUs that have at least 24 GB, such as A100 (40 GB) or V100 (32 GB), and request one GPU. This provides the necessary memory without requiring multi-GPU programming.

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Configure the pod to request `nvidia.com/gpu: 2` in its resource limits.

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Increase the memory limit of the pod to 24 GB and set the `NVIDIA_VISIBLE_DEVICES` environment variable to `all`.

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Enable GPU sharing by setting the `nvidia.com/gpu` resource to `0.5` to allow two pods to share a GPU.

Question 14

You are designing an Azure Cosmos DB for NoSQL container to store vector embeddings for a product recommendation engine. Queries will filter on category and then perform vector similarity search. You need to minimize latency and ensure the index supports both operations efficiently. Which indexing policy should you implement?

Accepted Answer

Include the category path in the range index and define a vector index on the embedding property.. For hybrid queries that filter on a scalar property and then perform vector search, the indexing policy must include the scalar property in the range index and define a vector index on the embedding property. This allows the query engine to apply the filter first, reducing the set of documents for vector comparison, which improves performance and lowers RU costs.

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Exclude all paths except the vector property and create a vector index on it.

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Use only the default indexing policy, which automatically indexes all properties including vectors.

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Create a composite index on category and the vector property.

Question 15

You are building a RAG solution using Azure AI Search. Documents are updated frequently, and you need each document's vector embedding to be regenerated automatically whenever its text changes, without writing custom code to detect changes. Which Azure AI Search feature should you configure?

Accepted Answer

An indexer with a skillset that includes the Azure OpenAI Embedding skill.. An indexer with the Azure OpenAI Embedding skill automatically chunks and vectorizes source content during indexing. When paired with a change detection policy (such as a high-water mark column or change tracking), only modified documents are reprocessed, keeping embeddings synchronized without custom code. This is the standard no-code approach for maintaining vector freshness in Azure AI Search.

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A synonym map that links text terms to their vector representations.

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A scoring profile that boosts documents with recently updated timestamps.

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A custom Web API skill that calls Azure OpenAI directly and writes vectors to the index.