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| Section | Objectives |
|---|---|
| Topic 1: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 76
You process Azure Service Bus messages that require a dependent external API call.
If the API is temporarily unavailable, you must delay processing of the message without incrementing the delivery count.
You need to find a way to process the message when the API is available while keeping the message accessible.
Which message action should you perform?
Answer: B
NEW QUESTION # 77
You need to improve throughput for concurrent application requests to PostgreSQL.
What should you implement?
Answer: D
Explanation:
The correct solution is to implement connection pooling , ideally by using the built-in PgBouncer capability in Azure Database for PostgreSQL Flexible Server. Connection pooling allows many concurrent application requests to share and reuse a smaller set of established PostgreSQL server connections instead of repeatedly opening and closing database sessions. Microsoft notes that creating a new PostgreSQL connection for each operation consumes server resources because each connection involves its own backend process. Reusing pooled connections therefore reduces connection overhead, lowers CPU and memory consumption, and improves throughput for high-concurrency workloads.
This directly matches Fabrikam ' s stated requirement that database connections support high concurrency with minimal latency through connection optimization . PgBouncer can efficiently multiplex large numbers of client connections onto fewer PostgreSQL connections, particularly in transaction pooling mode.
Increasing max_connections is not the preferred solution because each additional PostgreSQL connection consumes memory and other resources; Microsoft explicitly recommends PgBouncer instead when more concurrent connections are required. Increasing shared_buffers addresses caching and memory allocation rather than connection-management overhead. Read replicas can scale read workloads, but they do not optimize the application ' s connection lifecycle.
Study Guide references: Azure Database for PostgreSQL Flexible Server # connection pooling; PgBouncer; connection management and high-concurrency performance.
NEW QUESTION # 78
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a downstream processor.
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.
Answer:
Explanation:
Explanation:
Verified answer: 1) Register the Event Grid resource provider; 2) create a custom topic; 3) create an event subscription.
Detailed Explanation: A newly provisioned subscription must have the Event Grid resource provider available before Event Grid resources can be created. The publisher needs a custom topic as the event-ingress resource, and routing to a downstream processor is then defined by an event subscription on that topic. The event subscription can include filters such as event type or data fields so that only high-confidence events reach the processor. Creating a partner topic or domain is unnecessary for the stated custom workflow.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Create an Event Grid custom topic or domain
NEW QUESTION # 79
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 KOL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
To satisfy both requirements with the minimum number of statements using the Kusto Query Language (KQL), you should use the following KQL operators:
where: Filters data by time range (ago(1h)) and request status (success == false).
join: Correlates the requests and dependencies tables using a unique identifier (usually operation_Id).
summarize: Computes the average (avg()) dependency duration grouped by the specific operation (operation_Name).
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/application-insights-faq
NEW QUESTION # 80
A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts.
What should you implement?
Answer: A
Explanation:
Connect the agent to an Azure AI Search knowledge source and explicitly constrain its instructions to answer from retrieved evidence. Microsoft Foundry Agent Service can use Azure AI Search to retrieve proprietary organizational content and ground model responses in that content. Retrieved documents provide the factual context from which the model generates its answer, and Azure AI Search can return source references for attribution.
Microsoft ' s RAG guidance also specifically recommends using clear system instructions that require the model to stay within retrieved content when hallucination risk must be reduced. If the available evidence does not support an answer, the instructions should direct the agent to state that the information is unavailable rather than inventing facts. Retrieval grounding substantially reduces unsupported generation, although production systems should still evaluate outputs because grounding alone does not mathematically guarantee zero hallucinations.
Increasing temperature would generally increase output variability and is contrary to the objective. A larger context window without retrieval does not give the model access to the organization ' s internal knowledge base. Disabling content filtering weakens safety controls and has no role in factual grounding.
Study Guide references: Microsoft Foundry Agent Service # Azure AI Search tool; RAG grounding; knowledge sources; source attribution; grounding instructions.
NEW QUESTION # 81
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