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| Section | Objectives |
|---|---|
| Topic 1: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 2: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 14
An AI application uses a database. The database credential rotates every 30 days.
The application currently requires a manual update each time the credential rotates.
You need to ensure that the application always uses the latest secret version without manual updates.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: C,D
NEW QUESTION # 15
You optimize an AI inference API that uses Redis caching.
You must reduce the risk of serving outdated data while minimizing cache management overhead.
You need to implement the caching strategy that satisfies the requirements.
What should you do?
Answer: A
Explanation:
Trigger invalidation when source data changes is the best action.
Data Freshness: This is a proactive cache-aside or write-through approach. By explicitly deleting or updating the specific Redis key when the underlying database or model data changes, you completely eliminate the risk of serving stale data.
Low Overhead: It keeps cache management overhead minimal because invalidation logic is execution-dependent (event-driven). Instead of constantly updating expirations or relying on heavy computing logic, you only trigger an action when a write/change event occurs.
Incorrect:
[not B]
Set eviction policy to allkeys-lru:
This policy is a memory management feature, not a data-freshness strategy. It only deletes the Least Recently Used (LRU) keys when your Redis instance runs out of RAM. If memory usage is stable, outdated data will sit in the cache forever, posing a major risk of serving stale inferences.
[not D]
Reset expiration on each read (Sliding TTL):
This actively increases the risk of serving outdated data. If a specific AI response is highly popular and read continuously, its expiration window will extend indefinitely. The data remains stuck in the cache even if the source data undergoes critical updates.
Reference:
https://oneuptime.com/blog/post/2026-01-30-llm-caching-strategies/view
NEW QUESTION # 16
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?
Answer: 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.
Reference:
https://aws.plainenglish.io/why-your-kubernetes-pod-is-crashing-and-how-to-fix-it-5c71f8d2779a?gi=a46b335270ff
NEW QUESTION # 17
You are configuring sampling for a distributed application that sends traces to Azure Monitor. The solution must:
* Preserve upstream sampling decisions across distributed traces
* Capture all spans during local testing.
* Sample 10 percent of traces in production.
You need to apply the appropriate sampling configuration for each requirement.
What should you do? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once or not at all. You may need to move the split bar Between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Preserve parent sampling decisions: ParentBasedSampler. Capture all spans locally:
AlwaysOnSampler. Sample 10% in production: TraceIdRatioBasedSampler(0.1).
Detailed Explanation: Parent-based sampling propagates the sampling decision from the upstream parent, preventing broken sampling decisions across a distributed trace. AlwaysOnSampler records every span, which is suitable for local test environments where complete trace visibility is more important than telemetry volume. TraceIdRatioBasedSampler with 0.1 selects approximately ten percent of traces based on trace identifiers, providing deterministic fixed-ratio sampling for production. A batch span processor and an exporter control processing/export, not the sampling decision itself.
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 | OpenTelemetry sampling in Azure Monitor | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 18
An application deployed to AKS depends on an internal API hosted in the same cluster. All pods are healthy and report Ready status, but requests between the services time out.
You need to determine whether the issue is related to Kubernetes service configuration or application code.
What should you do first?
Answer: A
NEW QUESTION # 19
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