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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
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NEW QUESTION # 47
You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?
Answer: D
Explanation:
You should set the input_fidelity parameter to "high" in this scenario.
In Microsoft Foundry (built on Azure AI Foundry), the input_fidelity parameter specifically dictates how strictly the image generation model adheres to the visual features, layout, and style of the source image.
Why "high" is the Correct Choice
Preserves Product Identity: Setting input_fidelity="high" ensures that critical branding details, logos, structures, and product contours are preserved exactly as they appear in the original photo.
Designed for E-commerce: Microsoft explicitly designed this setting for e-commerce, commercial marketing assets, and product photography to prevent the model from hallucinating or altering key product characteristics.
Model Support: This parameter is natively supported when configuring agent image generation tools (such as the ImageGenerationTool or ImageGenTool classes in the Foundry Agent Service) leveraging advanced models like GPT-image-1 or GPT-image-2.
Reference:
https://developers.openai.com/cookbook/examples/generate_images_with_high_input_fidelity
NEW QUESTION # 48
Hotspot Question
You have a Microsoft Foundry project that contains a customer support application.
You create an evaluation named Run1 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a medium severity threshold You create an evaluation named Run2 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a high severity threshold
You run both evaluations against a dataset named DB1 and receive the following results:
- Content harm defect rate of Run1: 12%
- Content harm defect rate of Run2: 4%
- Protected material evaluation of Run1: 6%
- Protected material evaluation of Run1: 6%
You start a fine-tuning job by using DB1. The job fails during automatic RAI checks for multiple content harm types.
You discover that the content filtering configuration is set to high severity.
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:
Box 1: No
Changing the content filtering configuration to low severity will not resolve the fine-tuning job issues.
Box 2: Yes
The difference between a 12% defect rate in Run1 and a 4% defect rate in Run2 is entirely consistent with the different severity thresholds configured for the two evaluations.
Box 3: Yes
The protected material evaluation metric is entirely unaffected by changes to the content harm severity threshold.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-evaluation/azure.ai.evaluation
NEW QUESTION # 49
You need to recommend a solution to assess the responses generated by Agent1 when the agent uses the product information stored in storage1. The solution must meet the technical requirements.
Answer: C
Explanation:
A Retrieval Augmented Generation evaluator is required because Agent1 will retrieve product-sheet content and use it as grounding context when generating answers. Foundry RAG evaluators assess both retrieval quality and the quality of the final grounded response. This directly supports Contoso's requirement that answers based on the product sheets be relevant, complete, and accurate.
The appropriate evaluation suite can include groundedness , which determines whether the answer is supported by the retrieved product information without fabrication; relevance , which evaluates whether the answer accurately and completely addresses the customer's question; and response completeness , which measures whether critical expected information is missing. Retrieval evaluators can additionally determine whether the indexing and search pipeline returned context chunks relevant to the query.
A coherence evaluator assesses readability and logical organization but does not establish that claims are supported by storage1. A custom guardrail controls or blocks behavior rather than measuring RAG quality.
Fine-tuning changes model behavior and does not evaluate whether retrieved documentation was selected and used correctly.
The AI-103 Study Guide includes evaluating models and applications for fabrications, relevance, quality, and safety , as well as monitoring grounding quality and retrieval relevance.
NEW QUESTION # 50
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 # 51
You have an Azure subscription.
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
What should you use?
Answer: A
Explanation:
To deploy the DALL-E model, you need Microsoft Foundry and the Azure Command Line Interface (CLI).
Deployment vs. Application Logic: The Azure CLI along with Microsoft Foundry handles the administrative infrastructure layer required to provision, configure, and manage model deployments within your subscription.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/create-resource
NEW QUESTION # 52
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