Developing AI Cloud Solutions on Azure AI-200 exam torrent materials
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Microsoft AI-200 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* 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.
Your solution must answer questions about numeric data in large Excel-based reports, for example:
" What was Q3 revenue for the West region? "
Which approach is most appropriate?
- A. Convert data to a structured table/SQL source and use a function-calling agent to query it
- B. Store the Excel data as unstructured text and embed it for vector search
- C. Rely on the model ' s parametric knowledge
- D. Use Azure AI Vision to read the spreadsheet as an image
Correct Answer: A 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
You are implementing the expiration requirement for AI-generated summaries in the Redis cache.
You need to purge the AI summaries five minutes after creation, regardless of how often they are accessed.
What should you do?
- A. Use the PERSIST command after each successful read operation
- B. Implement sliding expiration on each cache key
- C. Configure absolute expiration on each cache key
- D. Set the maxmemory-policy to allkeys-lru
Correct Answer: C 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
You have an existing AKS cluster and a container image stored in Azure Container Registry.
You must deploy a new version by using a Deployment manifest that supports rolling updates 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.
Correct Answer:
Explanation:
You are using the Python SDK to develop a containerized AI application that retrieves the value of a runtime setting stored in an Azure App Configuration resource. The application will run in Azure in the security context of a managed identity.
The application must work in a local development environment without any code changes.
You need to complete the code that implements the retrieval.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Verified Answer: var1 = DefaultAzureCredential(); retrieval call = get_configuration_setting(...).
Detailed Explanation: DefaultAzureCredential is designed to support the same application code across local development and Azure-hosted environments. Locally it can use developer credentials, while in Azure it can use the workload's managed identity. The App Configuration client's get_configuration_setting method retrieves a specific key/label pair, which matches the code shown. Using ManagedIdentityCredential directly would not provide the same local-development credential chain without code or environment-specific handling.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Azure App Configuration Python client | Managed identities for Azure resources
