Microsoft AI-200 Exam With Confidence Using Practice Dumps

Initialize the connection by using the account endpoint and key: CosmosClient

Define shared throughput: Database

Perform item CRUD operations: Container

The Azure Cosmos DB for NoSQL .NET SDK uses a clear resource hierarchy. CosmosClient is the top-level client object and is responsible for connecting to the Azure Cosmos DB account. Microsoft identifies it as the primary SDK class for accessing account-level resources and databases. It can be initialized by using the account endpoint together with an account key or another supported credential.

A Database represents a database resource within the account. Shared provisioned throughput is configured at the database level , allowing multiple containers within that database to share the same RU/s allocation. Microsoft documents that setting throughput on the database creates a shared-throughput database.

A Container is the SDK object used to interact with items stored in a Cosmos DB container. Microsoft specifically describes the Container class as the object used for item operations such as creating, reading, updating, deleting, and querying documents.

The Indexing policy option is not used for any of these requirements. It controls how Cosmos DB indexes document properties for query execution, not connectivity, shared throughput, or CRUD access.

Study Guide references: Azure Cosmos DB for NoSQL .NET SDK → CosmosClient; Database; shared throughput; Container; item CRUD operations.

Trigger scaling based on queue depth: Azure Service Bus scaler

Deallocate instances when the message queue is empty: Minimum replicas = 0

Prevent uncontrolled burst scaling: Maximum replicas = 10

The batch retraining workload must scale according to queue length , so the correct KEDA trigger is the Azure Service Bus scaler . KEDA integrates with Azure Service Bus and monitors queue or subscription metrics, allowing AKS workloads to scale according to the number of pending messages rather than CPU utilization or HTTP request concurrency. Microsoft documents KEDA as the event-driven autoscaling mechanism for AKS and specifically supports Azure-based scalers for queue-driven workloads.

To meet the requirement to deallocate workload instances when no messages remain, configure minimum replicas = 0 . KEDA supports scale-to-zero for event-driven workloads, which is particularly appropriate for intermittent batch or machine-learning processing because no pods need to remain running while the queue is empty. Microsoft specifically identifies scale-to-zero as a KEDA capability and recommends it for sporadic ML workloads where cost optimization is important.

Finally, configure maximum replicas = 10 . A finite maximum replica count establishes an explicit upper scaling boundary, preventing large queue bursts from creating an uncontrolled number of worker replicas and protecting both infrastructure cost and downstream services.

Study Guide references: AKS → KEDA; Azure Service Bus event-driven scaling; minReplicaCount; maxReplicaCount; scale-to-zero; autoscaling limits.