Splendid Microsoft AI-103 Exam Questions - Pass Exam Confidently [2026]

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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q41-Q46):

NEW QUESTION # 41
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAl v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?

Answer: B

Explanation:
The correct role is Cognitive Services OpenAl User . The application is using DefaultAzureCredential, so az login only proves the developer's Microsoft Entra identity and enables token acquisition. It does not by itself grant authorization to the model deployment. Azure OpenAI and Microsoft Foundry separate authentication from authorization; Microsoft Entra ID provides token-based authentication, while Azure RBAC controls whether the signed-in principal can perform data-plane actions such as model inference. Microsoft's Foundry guidance states that Microsoft Entra ID supports granular RBAC and that data-plane operations include runtime usage such as chat completions and embedding generation.
For Azure OpenAI resources, the Cognitive Services OpenAI User role specifically allows users to make inference API calls with Microsoft Entra ID against deployed models, while preventing higher-privilege actions such as creating deployments, copying keys, fine-tuning, or managing the resource.
The other roles are not least privilege. Contributor grants broad management-plane permissions. Cognitive Services User is broader and less specific than the Azure OpenAI inference role. Cognitive Services Data Reader is read-oriented and does not provide the required model inference data action. Reference topics:
Microsoft Foundry authentication and authorization, Azure RBAC, Microsoft Entra ID keyless authentication, Azure OpenAI v1 API, and least-privilege model inference.


NEW QUESTION # 42
You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.
You are approaching the expiration date of the custom speech to text model.
What is the expected behavior when the model expires?

Answer: C

Explanation:
The correct answer is A. Speech recognition requests will fall back to the most recent base model for the same locale . Microsoft's custom speech model lifecycle guidance states that when a model expires, it is no longer available for transcription. For the custom endpoint route, speech recognition requests fall back to the most recent base model for the same locale. The documentation also warns that recognition results might still be returned, but the transcription may no longer reflect the domain-specific adaptation of the custom model.
This distinguishes real-time custom endpoint behavior from batch transcription behavior. Batch transcription requests that specify an expired model fail with a 4xx error, but that is not the route described in this question.
The agent is using a custom endpoint for real-time recognition, so fallback to the latest base model is the expected behavior. The model is not automatically deleted merely because it expires, and it does not continue to use the expired custom model indefinitely. The operational recommendation is to update the endpoint's model before expiration by redeploying the endpoint with a newer custom model. Reference topics: Azure Speech custom speech model lifecycle, custom endpoints, model expiration, real-time speech recognition, and endpoint redeployment.


NEW QUESTION # 43
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You add a reflection pass that regenerates the response if the required clauses are missing.
Does this meet the goal?

Answer: A

Explanation:
Yes, the solution meets the goal. The problem is not retrieval availability, because the required regulatory clauses are already present in the retrieved policy documents. The failure occurs during generation: the agent produces a summary that omits required content. A reflection pass is the correct application-level control because it adds a verification step before the response is returned. The pass can compare the draft answer against the retrieved clauses, detect missing mandatory content, and trigger regeneration or revision until the summary includes the required clauses.
This aligns with Microsoft Foundry's evaluation and observability model, where generated responses are assessed for reliability, groundedness, relevance, and quality throughout the AI application lifecycle. Foundry observability guidance describes evaluation as a mechanism for measuring response quality and improving AI outputs across development and production workflows. The Azure AI evaluation SDK also defines completeness as the extent to which a generated response contains all necessary and relevant information with respect to the provided ground truth. Reflection operationalizes that quality check inside the application flow, rather than merely reporting the defect after the fact. Reference topics: model reflection, response completeness, RAG generation quality, retrieved context verification, and agent response optimization.


NEW QUESTION # 44
Your company is piloting a customer support agent in a Microsoft Foundry project name Project1. Project1 is connected to an existing Application Insights resource, and the company ' s support team reviews runs in the Traces tab.
The Foundry Agent Service is configured to perform the following actions:
* Retrieve the Application Insights connection string by calling
project_client.telemetry.get_application_insights_connection_string().
* Call configure_azure_monitor(connection_string=...) to enable telemetry.
A separate LangChain service configured to use OpenTelemetry and has the following configurations:
* Uses AzureAIOpenTelemetryTracer(connection_string=..., enable_content_recording=False)
* Passes the tracer by using config={ " callbacks " :[azure_tracer]}
Company policy has the following requirements:
* Telemetry from LangChain and OpenTelemetry must be distinguishable within the same Application Insights resource.
* Secrets and credentials must NOT be stored in prompts, tool arguments, or span attributes.
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 LangChain service will appear in Traces without configuring a tracer: No Setting different OTEL_SERVICE_NAME values separates the services in Application Insights: Yes When using enable_content_recording=False, prompts and tool data will be captured in the telemetry: No The first statement is No because a separate LangChain or LangGraph application must emit telemetry through the configured tracing integration. Microsoft's LangChain tracing guidance states that you configure AzureAIOpenTelemetryTracer, attach it to the runnable or agent through callbacks, and then inspect the emitted traces in Azure Monitor. The troubleshooting guidance also states that missing LangChain or LangGraph spans are caused by tracing callbacks not being attached to the run.
The second statement is Yes . In OpenTelemetry, OTEL_SERVICE_NAME maps to the service.name resource attribute. Azure Monitor Application Insights uses cloud role names to represent separate services, and Microsoft states that when multiple services emit to the same Application Insights resource, cloud role names must be set so services are represented properly.
The third statement is No . enable_content_recording=False is specifically used to redact message content and tool call arguments from traces. Microsoft also advises disabling content recording in production and not storing secrets, credentials, or tokens in prompts or tool arguments. Reference topics: Microsoft Foundry tracing, LangChain tracing, OpenTelemetry service naming, Application Insights, and secure telemetry configuration.


NEW QUESTION # 45
You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding locations within the source document.
What should you do?

Answer: C

Explanation:
The correct answer is A. Enable estimateFieldSourceAndConfidence . Azure Content Understanding document analyzers support an opt-in confidence and grounding capability for field extraction. Microsoft documentation states that to opt in for confidence and grounding, you set estimateFieldSourceAndConfidence
= true in the analyzer configuration, or configure estimateSourceAndConfidence = true for specific fields.
This enables each extracted field to include a confidence score and references back to the original document source location.
This directly satisfies both stakeholder requirements: source grounding allows users to verify where the extracted value came from in the scanned PDF, and the confidence score supports downstream automation rules, such as sending low-confidence fields to manual review. Microsoft's analyzer improvement guidance describes confidence scoring as a value between 0 and 1 and grounding as references or citations for extracted outputs to the original document content.
Generative extraction does not guarantee per-field confidence and source grounding. enableSegment is used for document segmentation, not confidence scoring. Labeled samples can improve extraction quality, but they do not by themselves enable confidence and grounding output. Reference topics: Content Understanding document analyzers, field extraction, confidence scoring, grounding, and RAG ingestion.


NEW QUESTION # 46
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