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| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI |
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
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NEW QUESTION # 105
You are reviewing the Python tracing configuration for an application that must send distributed traces to Azure Monitor.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
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:
* The code configures the tracer provider before any spans are created: Yes
* The configuration exports traces synchronously: No
* The configuration enables export to Azure Monitor: Yes
The first statement is Yes. The code calls trace.set_tracer_provider(TracerProvider()) before obtaining the tracer and before any span-creation operation occurs. In OpenTelemetry, retrieving a tracer by using trace.
get_tracer() does not itself create a span; spans are created later through methods such as start_span() or start_as_current_span().
The second statement is No. The code uses BatchSpanProcessor, which batches completed spans and exports them through a background processing mechanism rather than exporting every span synchronously on the application ' s execution path. OpenTelemetry documentation states that BatchSpanProcessor batches ended spans before sending them to the configured exporter, and the Python implementation uses a background thread for export processing.
The third statement is Yes. AzureMonitorTraceExporter is instantiated with an Azure Monitor/Application Insights connection string and passed to BatchSpanProcessor. That processor is then registered with the tracer provider. Microsoft provides this same pattern specifically for exporting OpenTelemetry traces to Azure Monitor/Application Insights.
Study Guide references: Azure Monitor OpenTelemetry; TracerProvider; BatchSpanProcessor; AzureMonitorTraceExporter; distributed tracing.
NEW QUESTION # 106
Drag and Drop Question
A Python API retrieves a document from Azure Database for PostgreSQL by using a SQL statement. The API accepts the document ID from user input. The current implementation inserts the document ID directly into the SQL statement.
You need to secure the SQL statement execution by minimizing the possibility of SQL injection.
How should you modify the current implementation? 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:
Box 1: Use a parameterized query
To secure your SQL statement against SQL injection, you must replace direct string insertion with parameterized queries (also known as prepared statements).
Box 2: Pass the ID as an argument
The current implementation inserts the document ID directly into the SQL statement.
Box 3: Supply the parameter tuple to the SDK method
Supply parameter tuples: Pass the user input as a separate tuple or list argument into your database driver's execution method (e.g., cursor.execute(query, (document_id,))).
Reference:
https://www.stackhawk.com/blog/finding-and-fixing-sql-injection-vulnerabilities-in-flask-python/
NEW QUESTION # 107
You maintain multiple versions of a container image in Azure Container Registry.
The production deployment must always run the exact same image build even if tags are changed later.
You need to ensure predictable and immutable image selection during deployment.
What should you do?
Answer: D
Explanation:
Use the image ' s SHA-256 manifest digest when defining the production deployment. Azure Container Registry assigns every pushed image manifest a unique digest, and Microsoft explicitly states that pulling an image by digest guarantees the image version being retrieved , even if an identically named tag is later pushed to a different image. A digest reference has the form myregistry.azurecr.io/repository@sha256: < digest > .
Tags such as production or latest are mutable references . By default, a user or pipeline with sufficient permissions can push a different image under the same tag. Microsoft therefore warns against relying on reusable stable tags for production deployments when exact image reproducibility is required.
A scheduled rebuild also creates a new image artifact and therefore cannot guarantee that production executes the original build. In contrast, the manifest digest is content-addressed and resolves to the precise image manifest selected at deployment time.
Thus, for deterministic and immutable production image selection, reference the container image by its SHA digest rather than by a mutable tag .
Study Guide references: Azure Container Registry # image manifests and digests; image addressing; tagging
/versioning recommendations; immutable deployment references.
NEW QUESTION # 108
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: D
Explanation:
Detailed Explanation: If the priority is to minimize stale results, source-driven invalidation is the direct control: when the underlying record changes, delete the corresponding cache entry so the next request repopulates it from current data. Sliding expiration can keep a frequently accessed stale item alive, disabling expiration is worse, and an allkeys-LRU policy evicts according to memory pressure rather than data freshness. Invalidation therefore best addresses correctness without requiring constant cache-wide maintenance.
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 Managed Redis documentation
NEW QUESTION # 109
You are troubleshooting a production Azure OpenAI service that is experiencing intermittent connectivity issues. All telemetry is sent to a Log Analytics workspace.
You must write a KQL query that analyzes the AppRequests table. The query must filter for requests that occurred within the last 30 minutes and have a failed status.
The final output must display the total number of failures for each unique operation.
You need to select the KQL operators required to filter the time range and aggregate the failure counts.
Which two operators should you use? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,C
NEW QUESTION # 110
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