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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 2: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 5: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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NEW QUESTION # 35
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAl model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
Answer: B
Explanation:
The correct answer is A. Implement a retry policy that uses exponential backoff and jitter . HTTP 429 indicates that the request rate or token rate has exceeded the configured service limits for the model deployment. Microsoft Foundry Agent Service limits guidance specifically recommends implementing exponential backoff with jitter in application retry logic when agents receive rate-limit 429 errors. It also recommends reviewing Azure OpenAI quotas and token-per-minute and request-per-minute limits for the deployment.
This approach improves reliability while remaining within service limits because retries are delayed progressively instead of immediately adding more pressure to an already throttled deployment. Microsoft Foundry Models quota guidance also states that unsuccessful requests still count toward per-minute rate limits and that continuously resending requests without backing off makes throttling worse. It recommends retry logic with exponential backoff and using the Retry-After header when available.
Creating a new thread and retrying immediately does not change the deployment's rate limits and can worsen throttling. Reducing registered tools may simplify orchestration but does not directly address model RPM or TPM limits. Splitting uploaded content into smaller files may help ingestion scenarios, but it is not the correct throttling control for intermittent HTTP 429 model calls. Reference topics: Foundry Agent Service limits, Azure OpenAI quota management, throttling, retry policies, and production reliability.
NEW QUESTION # 36
You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
The correct components are OpenTelemetry and Application Insights . Microsoft Foundry tracing for prompt agents is designed to capture detailed telemetry for agent execution, including latency, exceptions, prompt activity, and retrieval operations. For a Python service that invokes the agent outside the Foundry portal, OpenTelemetry is the appropriate instrumentation mechanism because the Foundry SDK tracing setup uses OpenTelemetry packages and Azure SDK tracing integration for client-side traces. This enables distributed tracing across the external Python service and the agent run.
Application Insights is the telemetry backend used by Foundry tracing. The Foundry tracing setup requires an Azure Monitor Application Insights resource to store traces, and traces can then be viewed in Foundry or directly in Azure Monitor Application Insights. Application Insights also provides performance and failure investigation experiences for response times, slow transactions, errors, and exceptions.
A Log Analytics workspace may underlie Application Insights data storage, but it is not the agent tracing component itself. The Azure Monitor Agent collects machine and guest telemetry, not application-level agent traces from a Python SDK invocation. Microsoft Sentinel is a security information and event management solution, not an end-to-end agent tracing mechanism. Reference topics: Microsoft Foundry tracing, Azure Monitor Application Insights, OpenTelemetry instrumentation, prompt agent observability, and Python SDK telemetry.
NEW QUESTION # 37
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support real-time inference. The solution must meet the following requirements:
- Use key-based authentication.
- Support real-time REST API access.
- NOT consume the vCPU quota of the virtual machines in the Azure
subscription.
Which type of deployment should you use?
Answer: B
Explanation:
The most appropriate type of deployment is a serverless API deployment (also referred to as a standard deployment or Models-as-a-Service / MaaS).
Key-Based Authentication: Serverless API deployments natively provision an endpoint URL alongside primary and secondary API keys to secure your client applications.
Real-Time REST API Access: When the model is successfully deployed, it exposes a scalable, real-time HTTP/REST endpoint matching the standardized Azure AI Model Inference API.
No Virtual Machine vCPU Quota Consumption: Unlike managed compute deployments-which provision dedicated virtual machines in your subscription and require VM vCPU quota- serverless API deployments are completely hosted and managed by Microsoft. They operate on a pay-as-you-go, token-based billing structure and do not consume any VM vCPU quota from your Azure subscription.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/deployments-overview
NEW QUESTION # 38
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: D
Explanation:
To enhance response completeness in your Microsoft Foundry agent, you must intercept the retrieved documents and the generated summary within your backend application logic before returning the payload to the user.
1. Implement Completeness Verification Logic
Add a verification step in your orchestration code (e.g., in your Python/Semantic Kernel or LangChain pipeline) that compares the generated summary against the retrieved chunks.' Map Key Assertions: Extract main policy rules from retrieved text.Cross-Reference Entities: Verify all key entities are in the summary.
Check Scope Coverage: Ensure every retrieved document is represented.
Scan for Gaps: Identify critical missing constraints or exceptions.
2. Apply Application-Level Mitigation Strategies
If the verification step detects that the summary is incomplete, use your code to correct it before the final response leaves your system.
Reference:
https://dev.to/moonrunnerkc/how-i-built-a-verification-layer-for-copilot-clis-multi-agent-output-4b7h
NEW QUESTION # 39
You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
- Multi-turn continuity within the session
- Cross-session continuity for the same case
- Access to the full interaction history, including user messages,
agent messages, tool calls, and tool outputs
You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?
Answer: C
Explanation:
To achieve full, multi-turn, and cross-session continuity with complete historical context (including user messages, agent messages, tool calls, and tool outputs), you must leverage the durable Conversation lifecycle provided by the Microsoft Foundry Agent Service runtime.
Because the underlying Agent Service can be stateless at the inference tier, managing continuity across days requires linking your agent interactions to a persistent, server-side conversation ID.
Here is exactly what must be done to meet your requirements:
*-> 1. Maintain a Durable Conversation ID
Instead of creating a brand-new conversation session every time a user returns, your application must persist and map the unique support case to a Microsoft Foundry Conversation ID (typically prefixed with resp_* or conv_*) in your external database.
New Case: Create a new session and capture the generated ConversationId.
Returning Case: Retrieve the previously saved ConversationId associated with that customer's support case from your database.
2. Resume Sessions via the SDK
When the customer returns days later, initialize the agent's session by passing the existing Conversation ID into the session creation method. This forces the Microsoft Foundry Agent Service to automatically reload the complete, un-summarized thread history on the backend before processing the next turn.
Reference:
https://learn.microsoft.com/en-us/agent-framework/agents/conversations/session
NEW QUESTION # 40
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