AI-200 Practice Test 22 — 15 Questions

Question 1

You are troubleshooting a performance degradation in an Azure Cosmos DB for NoSQL vector search application. Which THREE diagnostic steps or metrics should you inspect? (Choose three)

Accepted Answer

Normalized RU consumption across physical partitions to check for hot spotting. Option A is correct because Normalized RU consumption across physical partitions is the key Azure Cosmos DB metric for detecting hot spotting; if one physical partition consistently shows much higher normalized RU usage than others, vector search throughput is throttled by that partition. Option D is correct because the RU charge returned in each query response header (x-ms-request-charge) reveals the actual cost of each vector query, letting you identify expensive queries and compare them against provisioned throughput. Option E is correct because vector indexing in Cosmos DB for NoSQL is performed by an index transformation, and if that transformation is still in progress or stuck, vector queries may scan more data, return incomplete results, or run slower, so checking index transformation progress is essential. Option B is not relevant because Blob Storage access tiers (Hot vs Cool) affect blob storage cost and latency, not Cosmos DB vector query performance. Option C is not relevant because App Service SSL certificate expiration affects HTTPS connectivity to a web app, not the internal performance of Cosmos DB vector search.

Answer

Azure Blob Storage tier status (Hot vs Cool)

Answer

App Service SSL certificate expiration date

Question 2

Your organization is implementing strict compliance standards. All event payloads transmitted through Azure Event Grid must be encrypted in transit using TLS 1.3 and encrypted at rest using a customer-managed key (CMK). Which Azure Event Grid tier supports configuring customer-managed keys?

Accepted Answer

Event Grid Premium tier. Customer-managed keys (CMK) for encryption at rest are supported only on the Event Grid Premium tier. Premium also supports Private Link, managed identity delivery, and higher throughput. Standard and Basic tiers do not support CMK configuration.

Answer

Event Grid Standard tier

Answer

Event Grid Basic tier

Answer

Event Grid Basic tier with custom schema extensions

Question 3

You need to deploy a microservice application that consists of an API gateway and an AI model inference container. The solution must scale automatically based on HTTP traffic and support zero scaling when idle to reduce costs, without requiring complex Kubernetes cluster management. Which service is best suited for this?

Accepted Answer

Azure Container Apps. Azure Container Apps is a serverless container platform built on Kubernetes and KEDA that supports HTTP-based autoscaling, including scale-to-zero when idle. It abstracts away cluster management, making it ideal for microservices with an API gateway and an AI inference container that must scale on demand without operational overhead.

Answer

Azure App Service for Containers

Answer

Azure Batch

Answer

Azure Kubernetes Service (AKS)

Question 4

You are building an AI search solution in Azure AI Search. You need to ensure that when documents are deleted from the primary database, they are also automatically removed from the search index without requiring manual API calls. How should you configure the indexer?

Accepted Answer

Configure a soft-delete policy on the indexer referencing a deletion tracking column or flag in the data source. Configuring a soft-delete policy on the indexer referencing a deletion tracking column or flag in the data source allows Azure AI Search to automatically detect and remove documents from the index when they are deleted or marked as deleted in the source. This is the native, automated way to handle deletions without manual API calls.

Answer

Write a custom Azure Function triggered by every delete event to issue an HTTP DELETE to the search index

Answer

Re-create the entire search index from scratch daily

Answer

Set the index retention policy to auto-purge every 24 hours

Question 5

You have an AI scoring script that runs periodically. You want to execute this containerized workload on a schedule without managing virtual machines. Which Azure service is best suited for running containerized batch jobs on a schedule?

Accepted Answer

Azure Container Apps Jobs. Azure Container Apps Jobs is correct because it is a serverless container execution service that supports scheduled (cron) and event-driven jobs without managing VMs or Kubernetes infrastructure. You define a container image, a cron schedule, and resource limits, and Azure handles the orchestration, scaling, and execution — ideal for periodic AI scoring scripts packaged as containers.

Answer

Azure Kubernetes Service CronJobs

Answer

Azure Container Instances with restart policy Always

Answer

Azure Functions with HTTP triggers

Question 6

When designing a hybrid search architecture that integrates Azure AI Search with an Azure OpenAI embedding model, which THREE challenges must you typically address regarding data management and retrieval? (Choose three)

Accepted Answer

Ensuring model version consistency between ingestion embeddings and query embeddings. Option A is correct because vector search only returns meaningful results when the query embedding is generated by the same model and version that produced the document embeddings; mixing model versions or dimensions causes similarity scores to be invalid, so you must pin and track the embedding deployment/version across ingestion and query paths. Option B is correct because when source documents change or the index schema (fields, analyzers, vector profiles, dimensions) is modified, you must plan re-indexing or incremental refresh strategies to keep the index synchronized and avoid stale or orphaned vectors. Option D is correct because chunk size and overlap directly affect retrieval quality: chunks that are too large dilute the embedding's semantic focus, while chunks that are too small lose context, and overlap controls continuity across boundaries, so tuning these values is a core data-management decision. Option C is not needed because vector distance calculations are performed natively by Azure AI Search's vector search capabilities, not by provisioning HDInsight Hadoop clusters. Option E is not relevant because Azure SQL Server foreign key cascading deletes concern relational data integrity and have no role in embedding generation, vector indexing, or hybrid retrieval in Azure AI Search.

Answer

Provisioning HDInsight Hadoop clusters for vector distance calculations

Answer

Configuring Azure SQL Server relational foreign key cascading deletes

Question 7

You are troubleshooting a scenario where hybrid queries in Azure AI Search are returning unexpected relevance rankings. Which THREE factors could influence the final score when using hybrid search with semantic ranking? (Choose three)

Accepted Answer

Semantic ranker deep learning re-scoring. Option B is correct because in hybrid search with semantic ranking, the semantic ranker applies deep learning models to re-score the top results returned by the initial retrieval, directly influencing the final relevance ranking. Option C is correct because hybrid search combines keyword-based BM25 scoring with vector similarity, so the BM25 keyword match relevance scores contribute to the fused ranking. Option D is correct because Reciprocal Rank Fusion (RRF) is the algorithm Azure AI Search uses to merge keyword and vector result sets, and its rank-based combination determines the fused score that feeds into semantic re-ranking. Option A is incorrect because the CPU core count of the client application has no bearing on how Azure AI Search computes relevance scores server-side. Option E is incorrect because Blob Storage access tiers affect storage cost and latency, not the relevance scoring pipeline of a search index.

Answer

Virtual machine CPU core count assigned to the client application

Answer

Azure Blob Storage container access tier (Hot vs Cool)

Question 8

When configuring vector search in Azure AI Search, which TWO parameters must be defined when creating a vector profile? (Choose two)

Accepted Answer

algorithm. When creating a vector profile in Azure AI Search, the two required parameters are the profile's name and the algorithm configuration it references. Option B (name) is correct because every vector profile must have a unique name within the search index so it can be referenced by vector fields via the vectorSearchProfile property. Option A (algorithm) is correct because a vector profile must point to a configured vector search algorithm (for example, an HNSW or exhaustive KNN configuration defined in the index's algorithms collection) that determines how the vector index is built and queried. Options C (sqlConnectionString), D (vmSize), and E (cosmosDatabaseName) are incorrect because they are unrelated to Azure AI Search vector profile definitions: sqlConnectionString and cosmosDatabaseName pertain to data source connections for indexers, and vmSize relates to compute sizing in other Azure services, not to vector profile configuration.

Answer

sqlConnectionString

Answer

vmSize

Answer

cosmosDatabaseName

Question 9

Your development team is implementing an event-driven microservices architecture using Azure Service Bus. You need to ensure strict FIFO (First-In, First-Out) message processing order for related financial transaction events in an AI fraud-detection workflow. Which THREE configuration steps are required? (Choose three)

Accepted Answer

Implement a MessageSession receiver in your client application to process messages sequentially per session.. Option D is correct because sessions must be explicitly enabled on the Service Bus queue (via the EnableSession property) before session-based FIFO ordering can be used; without this, session IDs are ignored. Option B is correct because setting a Session ID on all related financial transaction messages groups them into the same session, guaranteeing that messages with the same SessionId are delivered in order. Option A is correct because the receiving client must use a MessageSession receiver (via AcceptMessageSessionAsync) to process each session's messages sequentially and in FIFO order. Option C is not required because partitioning is a throughput/availability feature and does not provide FIFO ordering; in fact, partitioned entities without sessions do not guarantee ordering. Option E is not required because duplicate detection only prevents duplicate message submission within the history window and has no role in enforcing FIFO processing order.

Answer

Configure partitioning on the Service Bus namespace tier.

Answer

Enable duplicate detection with a 10-minute history window.

Question 10

You are securing an Azure AI Search service. Which THREE of the following actions are recommended for implementing a robust security posture? (Choose THREE)

Accepted Answer

Enable Private Endpoint access and disable public network access.. Option B is correct because enabling a Private Endpoint for Azure AI Search creates a private IP connection via Azure Private Link, and setting public network access to Disabled ensures the service is only reachable from within the virtual network, eliminating exposure to the public internet. Option D is correct because Entra ID (formerly Azure AD) authentication allows callers to be authenticated with identity tokens and assigned granular roles, removing the need to distribute and rotate static admin or query API keys that can be leaked. Option E is correct because Azure RBAC (for example, Search Service Contributor, Search Index Data Contributor, and Search Index Data Reader roles) governs data plane and control plane access through Entra ID identities, enabling least-privilege and auditable access management. Option A is wrong because storing index data in a public blob container exposes sensitive content and is not how Azure AI Search stores or secures its indexes. Option C is wrong because admin API keys grant full control-plane rights (create, update, delete indexes) and should never be handed to read-only client applications; query keys or Entra ID-based read roles should be used instead.

Answer

Store all search index data in a public blob container for faster access.

Answer

Use an Admin API key for all read-only client applications.

Question 11

When configuring vector search in Azure AI Search, which TWO components are defined within the vectorSearch object of an index definition? (Choose two)

Accepted Answer

algorithms. Option C (algorithms) is correct because the vectorSearch object in an Azure AI Search index definition contains an algorithms array where you configure the approximate nearest neighbor (ANN) algorithm used for vector indexing, such as HNSW (with parameters like m, efConstruction, efSearch) or exhaustive KNN. Option D (profiles) is correct because the same vectorSearch object contains a profiles array that defines named vector search profiles, each combining an algorithm configuration with a vectorizer, which fields then reference via their vectorSearchProfile property. Options A (sqlPartitionKey), B (storageAccountConnectionString), and E (cosmosContainerName) are incorrect because they are not properties of the vectorSearch object; they relate to data source or indexer configuration for external data stores (e.g., Azure SQL or Cosmos DB), not to vector search configuration within an index definition.

Answer

sqlPartitionKey

Answer

storageAccountConnectionString

Answer

cosmosContainerName

Question 12

Your company policy requires securing AI model endpoints by disabling public internet access and ensuring all API requests use secure managed identities. Which TWO actions must you take? (Choose two)

Accepted Answer

Disable public network access on the Azure AI service resource and configure a private endpoint.. Option A is correct because disabling public network access on the Azure AI service resource and configuring a private endpoint ensures the endpoint is reachable only through a private IP address inside the virtual network, satisfying the requirement to remove public internet exposure. Option E is correct because assigning the appropriate Azure RBAC role (for example, Cognitive Services User or equivalent) to the application's managed identity on the AI service enables token-based, keyless authentication via Microsoft Entra ID, which is exactly how secure managed identities are used for API requests. Option B is wrong because an unencrypted HTTP binding violates the security requirement and offers no managed identity authentication. Option C is wrong because storing model weights and endpoint keys in public GitHub repositories exposes secrets and is a severe security violation. Option D is wrong because a long-lived SAS token is a shared secret credential, not a managed identity, and a 10-year expiration creates an unacceptable security risk.

Answer

Expose the AI endpoint via an unencrypted HTTP binding for maximum performance.

Answer

Store all AI model weights and endpoint keys in public GitHub repositories for CI/CD access.

Answer

Configure a shared access signature (SAS) token with a 10-year expiration period.

Question 13

You are architecting an event-driven AI ingestion pipeline using Azure Event Hubs. Which TWO features or capabilities are native to Azure Event Hubs? (Choose two)

Accepted Answer

Apache Kafka protocol endpoint compatibility. Option B is correct because Azure Event Hubs natively exposes a Kafka-compatible endpoint (port 9093, SASL/PLAIN or SASL/OAUTHBEARER), letting existing Apache Kafka producers and consumers connect without code changes. Option C is correct because Event Hubs Capture is a built-in feature that automatically archives streaming data to Azure Blob Storage or Azure Data Lake Storage in Avro format based on a time or size window, with no custom consumer required. Option A is not native: dead-lettering after a max delivery count is a Service Bus queue/subscription behavior, not an Event Hubs feature (Event Hubs uses consumer offsets and checkpointing). Option D is not native: webhook subscription validation handshakes belong to Event Grid event subscriptions, not Event Hubs. Option E is not native: AMQP 1.0 sessions with FIFO per session ID are a Service Bus messaging capability; Event Hubs provides partition-based ordering, not AMQP session management.

Answer

Automatic dead-letter queue routing after max delivery count

Answer

Webhook event schema subscription validation handshakes

Answer

AMQP 1.0 message session management for FIFO per session ID

Question 14

You are configuring comprehensive observability for an intelligent application built on Azure AI Document Intelligence and Azure OpenAI. Which THREE telemetry types or tools should you configure in Application Insights to capture complete operational insights? (Choose three)

Accepted Answer

Configure dependency tracking to monitor outgoing HTTP calls made to Azure AI REST endpoints.. Option A is correct because Application Insights dependency tracking automatically captures outgoing HTTP/REST calls to Azure AI Document Intelligence and Azure OpenAI endpoints, recording duration, result codes, and failures so you can diagnose latency and errors in the AI service calls. Option B is correct because custom events let you log domain-specific telemetry such as document processing success rates, which are not captured by default request/dependency metrics and are essential for business-level observability. Option D is correct because exception telemetry automatically captures unhandled runtime errors with stack traces, enabling root-cause analysis of failures in the application code. Option C is incorrect because Azure Firewall policy management is a networking security function and cannot be installed into the Application Insights SDK. Option E is incorrect because Microsoft Entra ID conditional access policies govern authentication/authorization and cannot restart unhealthy worker threads.

Answer

Install the Azure Firewall policy manager directly onto the Application Insights SDK.

Answer

Use Azure Active Directory conditional access policies to restart unhealthy worker threads.

Question 15

Which TWO mechanisms can be used to monitor and optimize costs associated with Azure AI services?

Accepted Answer

Azure Budgets with configured email alert notifications.. Azure Budgets (option D) let you set spending thresholds on a scope such as a resource group or subscription and trigger email alert notifications when actual or forecasted costs approach or exceed the budget, which directly supports monitoring and controlling Azure AI service spend. Azure Advisor cost recommendations (option E) analyze usage telemetry to surface right-sizing guidance and idle or underutilized resource cleanup, which is exactly the optimization mechanism for reducing AI service costs. The remaining options are unrelated to cost management: Network Watcher IP flow logs (A) capture network traffic metadata for connectivity and security troubleshooting, Application Insights live metrics stream (B) provides real-time performance and telemetry diagnostics for applications, and Azure Bastion session monitoring logs (C) record secure RDP/SSH session activity through Bastion hosts, none of which report or optimize billing costs.

Answer

Network Watcher IP flow logs.

Answer

Application Insights live metrics stream.

Answer

Azure Bastion session monitoring logs.