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| Section | Weight | Objectives |
|---|---|---|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
>> Valid Microsoft AI-200 Practice Materials <<
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NEW QUESTION # 39
Hotspot Question
You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.
The application must use managed identity for authentication.
You review the following code segment that retrieves a secret from Key Vault.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
No, this code will not retrieve the latest version of dbPassword.
Instead, it explicitly retrieves the specific version designated by the string "123".
To pull the latest active version of a secret, omit the version parameter entirely from the get_secret function.python# Change this line:
secret = client.get_secret("dbPassword", version="123")
# To this line:
secret = client.get_secret("dbPassword")
Box 2: Yes
Yes, the code will successfully authenticate to Azure Key Vault without storing client secrets when deployed to an Azure-hosted environment with a managed identity enabled.
DefaultAzureCredential Behavior: This class is a sequential chaining credential mechanism.
When deployed to Azure (such as Azure App Service, Azure VMs, or Azure Functions), it automatically looks for an active Managed Identity environment endpoint to fetch an access token.
Zero Secret Storage: Because the platform relies on the underlying Azure host infrastructure to handle identity token generation, you do not need to hardcode client secrets, passwords, or certificates anywhere inside your application code or configuration settings.
Box 3: No
No, subsequent calls to this code will not retrieve the new version.
By explicitly including version="123" in the get_secret method, you are requesting a specific immutable version of the secret.
Reference:
https://learn.microsoft.com/en-us/azure/key-vault/secrets/quick-create-python
NEW QUESTION # 40
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
* Correlate failed requests with dependency calls during the last hour.
* Calculate the average dependency duration per operation.
You need to construct the KQL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
Answer: A,B,C
Explanation:
Use where , join , and summarize .
The where operator filters telemetry to the required scope, including records from the last hour and failed requests. For example, a predicate can restrict timestamp > ago(1h) and request success status in the same filtering expression.
Next, use join to correlate request telemetry with dependency telemetry. Application Insights assigns related telemetry a common operation_Id , and Microsoft specifically demonstrates joining the dependencies and requests tables on operation_Id to identify dependency calls associated with requests.
Finally, use summarize with avg(duration) grouped by the appropriate operation identifier or operation name.
summarize performs aggregation over groups and is therefore the required operator for calculating average dependency duration. Microsoft documents dependency duration as supporting an Avg aggregation.
extend is unnecessary because no derived column is required, while distinct merely removes duplicate rows and cannot calculate averages or correlate two telemetry tables.
A representative structure is:
requests
| where timestamp > ago(1h) and success == false
| join (dependencies | where timestamp > ago(1h)) on operation_Id
| summarize avg_dependency_duration = avg(duration1) by operation_Name
Study Guide references: Azure Monitor Application Insights; KQL where; telemetry correlation using operation_Id; join; summarize; dependency duration analysis.
NEW QUESTION # 41
You need to configure vector embedding updates according to the business and technical requirements.
Which information should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Identify document changes: Change feed processor. Scale out vectorization processing:
Lease container.
Detailed Explanation: The change feed processor is designed to react to inserts and updates in a monitored Cosmos DB container, which directly matches the requirement to generate embeddings for new or changed documents. Its lease container stores processing state and coordinates work across multiple workers, allowing the processing workload to scale out without duplicating ownership of change-feed ranges. A periodic full scan would consume unnecessary RUs, and neither strong consistency nor container RU throughput is the coordination mechanism for distributed change-feed workers.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Azure Cosmos DB change feed processor
NEW QUESTION # 42
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.
Answer: 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
NEW QUESTION # 43
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 # 44
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