Groundedness Pro returns a binary pass or fail result and runs on the Azure AI Content Safety service, so it does not require you to deploy a model to act as a judge. That matches both requirements in the scenario.

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.

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.

Notifying users that their data has been processed by your sales system fulfills the Transparency responsible AI principle. Transparency ensures that users are informed about how their data is collected and processed, which helps foster understanding and build trust between users and technology providers.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/responsible-ai