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Microsoft AI-200 Exam Actual Questions
Developing AI Cloud Solutions on Azure (Page 2 )

Updated On: 11-Aug-2026
Page 2 of 10
PDF with 66 Questions

You maintain multiple versions of a container image in Azure Container Registry.
The production deployment must always run the exact same image build even if tags are changed later.
You need to ensure predictable and immutable image selection during deployment.
What should you do?

  1. Tag the image as production and deploy it by using the production tag.
  2. Schedule nightly rebuilds of the image.
  3. Configure deployment to use the latest tag.
  4. Identify the image by using its SHA digest.

Answer(s): D

Explanation:

Deploying a container image by its unique SHA-256 digest digest instead of its tag ensures a predictable and immutable deployment. Tags can be overwritten or moved to different image builds, but a digest is a cryptographic hash that never changes.


Reference:

https://container-registry.com/posts/container-image-versioning/



A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?

  1. Scale the deployment to more replicas.
  2. Decrease the initialDelaySeconds for the container liveness probe.
  3. Drain and reboot the node hosting the pod.
  4. Inspect the pod events and container logs.

Answer(s): D

Explanation:

Inspecting the pod events and container logs is the correct first action to take.
Because node-level metrics are normal, the issue is isolated to the specific workload or its individual container boundaries. Pod events will clarify exactly which health probe (liveness or readiness) is triggering the restart, and the logs will reveal why the application inside the container is failing to respond.

https://aws.plainenglish.io/why-your-kubernetes-pod-is-crashing-and-how-to-fix-it-5c71f8d2779a?
gi=a46b335270ff



You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Increase the scaling rule to allow for the maximum running replica count.
  2. Configure the scaling rule to allow for the termination of all active replicas.
  3. Configure a Kubernetes Event-driven Autoscaler rule that monitors queue length.
  4. Enable HTTP ingress concurrency scaling.

Answer(s): B,C

Explanation:

[C] You can configure an Azure Service Bus scale rule in Azure Container Apps, which utilizes built-in KEDA (Kubernetes Event-driven Autoscaling) to monitor queue length and scale the app from zero instances to meet your processing demands.
[B] Configure the scaling rule to allow for the termination of all active replicas is the correct action to take.
To ensure that no compute resources are consumed when the queue is empty, you must set the minimum replica count (minReplicas) to 0 in your Azure Container Apps scaling configuration.
When the Azure Service Bus queue has zero messages, KEDA will scale the container replicas down to zero, stopping all compute billing.


Reference:

https://learn.microsoft.com/en-us/azure/container-apps/scale-app



You configure ACR Tasks to automate image builds.
Container images must rebuild when:
-Application updates occur.
-Base image updates occur, such as when the underlying OS image is updated.
-Regular scheduled rebuilds are required.
You need to configure ACR Tasks to support automated image rebuilds.
Which three triggers should you configure? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Timer trigger
  2. Source code commit trigger
  3. Registry event trigger
  4. Base image update trigger
  5. Webhook notification trigger

Answer(s): A,B,D

Explanation:

To support automated container image rebuilds based on your requirements, you need to configure source triggers, base image triggers, and timer triggers in Azure Container Registry (ACR) Tasks.
Timer Triggers: Automates regular scheduled rebuilds. This uses a cron schedule expression to run image builds at specific intervals (e.g., weekly or monthly) regardless of code changes.
Source Triggers: Automates rebuilds when application updates occur. This monitors changes in your source code repository (like GitHub or Azure Repos) and fires a build when code is committed.
Base Image Triggers: Automates rebuilds when the underlying OS or framework image updates. ACR tracks dependencies and automatically kicks off a new build when your defined FROM image changes in the public registry or your private registry.


Reference:

https://oneuptime.com/blog/post/2026-02-16-how-to-set-up-acr-tasks-for-automated-container-image-builds-on-git-commit/view



DRAG DROP (Drag and Drop is not supported)
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Step 1: Enable ingress You must first expose your container app to external traffic to generate the default fully qualified domain name (FQDN) needed for DNS mapping.
Step 2: Add DNS records to the domain provider Log into your domain registrar to create the required TXT (for verification) and CNAME/A records pointing to your container app.
Step 3: Validate the custom domain name Azure checks your DNS records to confirm that you actually own the domain before allowing it to be linked.
Step 4: Add the custom domain name Once validation passes, you officially add and register the custom domain name within the Azure Container App configuration.
Step 5: Bind the certificate Finally, bind an SSL/TLS certificate to the custom domain to secure the connection and enable HTTPS access.


Reference:

https://learn.microsoft.com/en-us/azure/container-apps/ingress-overview



View Related Case Study

DRAG DROP (Drag and Drop is not supported)
You need to implement the semantic retrieval workflow for the recommendation engine to meet the technical and performance requirements of Fabrikam Inc.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Scenario: Current: The Recommendation engine is a customer-facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA).
Step 1: Define Table Schema with vector and metadata columns Schema configuration: Establish the PostgreSQL table structure using the pgvector extension to store both the product metadata and the high-dimensional embedding vectors.
Step 2: Configure a Hierachical Navigable Small World (HNSW) index on the embedding vector columns Configure Vector Index HNSW indexing: Implement a Hierarchical Navigable Small World (HNSW) index rather than a B-tree index, as standard B-tree indexing cannot index multi-dimensional vector embeddings for similarity searches.
An HNSW (Hierarchical Navigable Small World) index is the correct choice for the embedding vector columns in this scenario, while a B-tree index is entirely unsuited for vector similarity search.
Step 3: Load embedding vectors and associated product metadata Populate Database Data ingestion: Load the pre-computed embedding vectors along with their corresponding product metadata into the newly indexed table.
Step 4: Perform a similarity search using a WHERE clause and the <=> operator Query Similar Items Similarity search: Execute vector similarity queries using the <=> operator (which denotes cosine distance in pgvector) to find and return the closest product recommendations.


Reference:

https://mobisoftinfotech.com/resources/blog/enhancing-rag-generative-ai-postgresql-hnsw-indexes



View Related Case Study

You need to optimize vector search queries based on the technical requirements.
What should you do?

  1. Create a B-tree index on metadata filter columns.
  2. Increase the max_connections parameter.
  3. Increase the shared_buffers setting.
  4. Create an IVFFlat index on the embedding column.

Answer(s): A

Explanation:

Scenario: Technical requirements Performance: Semantic search latency must remain under 200 milliseconds at peak load. *-> Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints.
--To optimize vector similarity queries that must be performed only against products satisfying mandatory metadata constraints, you should Create a B-tree index on metadata filter columns.
1. Evaluate Query Execution OrderIn PostgreSQL with pgvector, combining metadata filters with vector similarity searches often triggers a multi-stage execution plan.
When metadata filtering is highly restrictive, creating a B-tree index allows the database engine to quickly narrow down the row scanned before or during the vector evaluation, preventing a costly full-table scan.
2. Assess Indexing Trade-offs B-tree Index (Metadata): Directly addresses the requirement that vector calculations must be performed only against products satisfying mandatory metadata constraints. It optimizes the metadata filtering step, significantly reducing compute overhead and isolating the target rows for vector processing.
IVFFlat Index (Vector): While an IVFFlat index speeds up high-dimensional approximate nearest neighbor (ANN) searches, it divides vectors into lists. If a metadata filter is applied after an IVFFlat index scan, it can lead to severe recall degradation or inaccurate results because rows matching the metadata might reside in unsearched vector lists. (
Note: For newer workloads, HNSW is generally preferred over IVFFlat for better recall and performance, but regular B-trees remain vital for the metadata layer).
3. Ensure Index Residency in RAM By isolating the dataset using a compact B-tree index on metadata columns, you minimize the active working set. This helps fulfill your operational requirement to ensure that high-dimensional vector indexes and target rows remain resident in RAM for efficient mathematical throughput.


Reference:

https://www.applied-ai.com/briefings/enterprise-rag-architecture/



View Related Case Study

HOTSPOT (Drag and Drop is not supported)
You need to optimize secure database connectivity from the containerized Recommendation API.
How should you configure the application? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: Use managed identity authentication. Scenario: Identity: Use managed identities for all service-to-service and service-to-database authentication. Plain-text credentials in configuration files are strictly prohibited.'
Box 2: Use a connection pooling library Scenario: Environment: Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
To support high-concurrency requests with minimal latency on Azure Database for PostgreSQL, the best action is to use a connection pooling library (or leverage Azure's built-in PgBouncer feature).
Eliminates Connection Overhead: PostgreSQL utilizes a process-per-connection model. Forking a new backend process for every incoming request introduces substantial CPU and memory overhead, severely degrading latency under high concurrency. A connection pool keeps a warm set of reusable database sessions active.
Optimized for Azure: Microsoft provides a built-in managed PgBouncer integration for Azure Database for PostgreSQL. Enabling it in transaction mode allows the database to accept thousands of concurrent client connections while keeping actual backend processes lean and stable
Box 3: Configure a maximum pool size Configure a maximum pool size is the best action to directly protect database stability during sudden traffic spikes.
Prevents Resource Exhaustion: Traffic spikes naturally lead to a surge in connection requests. Unchecked connections consume substantial RAM and CPU overhead, which can crash the database or trigger severe latency. Limiting the pool size stops the "thundering herd" problem
Acts as a Shock Absorber: When the pool hits its maximum limit, extra client requests are safely queued at the application or connection pooling layer (like Azure's built-in PgBouncer proxy) rather than overwhelming the database backend


Reference:

https://docs.azure.cn/en-us/postgresql/connectivity/concepts-pgbouncer https://learn.microsoft.com/en-au/answers/questions/5884412/best-practise-azure-postgresql-flexible-server-max



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