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Microsoft AI-200 Exam Prep
Developing AI Cloud Solutions on Azure (Page 5 )

Updated On: 10-Oct-2026
Page 5 of 27
PDF with 174 Questions

DRAG DROP (Drag and Drop is not supported)
You have an existing AKS cluster and a container image stored in Azure Container Registry.
You must deploy a new version without interrupting traffic.
You need to perform a rolling update by using a manifest file.
Which three 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:





Step 1: Update the image tag in the deployment manifest This changes the definition file to point to your new version image stored in the Azure Container Registry.
Step 2: Apply the updated manifest Running kubectl apply -f <manifest.yaml> triggers Kubernetes to natively initiate a zero-downtime rolling update strategy, incrementally replacing old pods with new ones.
Step 3: Verify rollout status Running kubectl rollout status deployment/<deployment-name> allows you to track and ensure the new version deploys successfully without failure.


Reference:

https://learn.microsoft.com/en-us/azure/aks/tutorial-kubernetes-deploy-application



DRAG DROP (Drag and Drop is not supported)
You are deploying an AI service to ACA.
The service must retrieve secrets securely from Key Vault by using managed identity.
You need to configure secure access.
Which three 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:





Step 1: Grant the Key Vault Secrets User role to the managed identity This uses Azure's modern role-based access control (RBAC) to give the application data-plane permission to read the secret values.
Step 2: Retrieve the secret by using the SDK Once authorized, the application code securely pulls the secret using libraries like the Azure Key Vault secret client library (such as SecretClient alongside DefaultAzureCredential).
Step 3: Assign a system-assigned managed identity Enabling a system-assigned identity provides the Azure Container App (ACA) with its own credentials managed automatically by Microsoft Entra ID, completely eliminating hardcoded credentials.


Reference:

https://learn.microsoft.com/en-us/azure/key-vault/general/tutorial-net-create-vault-azure-web-app



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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