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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q46-Q51):

NEW QUESTION # 46
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Al Search as the retriever.
You plan to ingest PDFs into an Azure Al Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?

Answer: A

Explanation:
The correct indexing approach is to use an indexer to extract image data into a normalized_images collection . In Azure AI Search enrichment pipelines, embedded images in PDFs are not passed directly from the text content field into OCR. Instead, the indexer must perform document cracking and image extraction by enabling the indexer image action. Microsoft's Azure AI Search documentation states that image-processing skills such as OCR and image analysis expect normalized images, and that enabling imageAction causes embedded images to be extracted and normalized for downstream skills.
The OCR skill is designed to receive image input from /document/normalized_images/*. Microsoft's skillset tutorial specifically states that the OCR skill assumes a normalized_images field exists and that this field is generated by setting the indexer imageAction configuration to generateNormalizedImages. The document extraction skill reference also confirms that generateNormalizedImages creates an array of normalized images during document cracking for OCR and image analysis.
Option A is incorrect because OCR does not run directly against the index content field. Option B maps outputs after enrichment; it does not extract images. Option D reshapes data but does not create the required normalized image collection. Reference topics: Azure AI Search indexers, AI enrichment, OCR skill, imageAction, and normalized_images.


NEW QUESTION # 47
You have a Microsoft Foundry project that contains an agent.
You use a GitHub Actions workflow for CI/CD.
You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Authentication method: An Azure Login action that uses OpenID Connect (OIDC) If the evaluation results are NOT met, configure the workflow to: Fail The correct authentication method is Azure Login with OpenID Connect (OIDC) . Microsoft Foundry's GitHub Actions evaluation guidance recommends Microsoft Entra ID authentication and states that authentication can be automated by using the Azure Login GitHub action with OpenID Connect. The sample evaluation workflow also grants id-token: write, runs azure/login@v2, and then invokes the Microsoft AI Agent Evaluation action. This is the appropriate CI/CD authentication pattern because it avoids long-lived personal access tokens and supports secure federated authentication from GitHub Actions into Azure.
The workflow should be configured to fail when evaluation thresholds are not met. Foundry's evaluation GitHub Action is designed to automate pre-production assessment of Microsoft Foundry agents in CI/CD pipelines and produce evaluation results for the configured evaluators and test dataset. A failed GitHub Actions check can then be enforced through branch protection so the PR cannot merge until the quality gate passes. Locking the target branch or sending an alert does not directly implement a CI quality gate. Reference topics: Microsoft Foundry agent evaluation, GitHub Actions evaluation workflow, Microsoft Entra authentication, Azure Login with OIDC, pull-request quality gates, and CI/CD governance.


NEW QUESTION # 48
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 # 49
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.


NEW QUESTION # 50
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: B

Explanation:
The correct approach is to create and reuse a conversation by storing the conversation's ID and supplying that ID on subsequent requests . In Microsoft Foundry Agent Service, conversations are durable objects with unique identifiers that can be reused across sessions. The official runtime guidance states that conversations store items, including messages, tool calls, tool outputs, and other data, and are intended for multi-turn continuity, cross-session continuity, and inspection of what happened over time. This directly satisfies the requirement to resume the same support case days later with the full historical context.
Persisting only the final model response is insufficient because it loses the full interaction chain, especially tool calls and tool outputs that may be essential to case state. Memory summarization is also not the best fit because the requirement asks for the complete history, not a compressed representation that may omit details.
Reusing the conversation ID allows Foundry to maintain the conversation server-side so the next turn can reuse prior context without the client manually rebuilding prompts. Reference topics: Foundry Agent Service runtime components, conversations, conversation items, multi-turn continuity, cross-session continuity, and tool output history.


NEW QUESTION # 51
......

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