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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 2: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 3: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 4: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 5: Implement computer vision solutions | 10-15% | - Analyze visual content
|
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NEW QUESTION # 14
Hotspot Question
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure AI Content Safety must access the images by using the blob URL.
The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 15
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search as the retriever.
You plan to ingest PDF into an Azure AI 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: B
Explanation:
The best configuration step an indexer to extract image data into a normalized_images collection.
Cracking Foundation: In Azure AI Search, document cracking automatically separates textual data from visual content. To make embedded images available to image-processing skills like OCR, the indexer must be explicitly configured with an imageAction configuration property (such as generateNormalizedImages or generateNormalizedImagePerPage).
Input Structure for OCR: This indexer step extracts embedded images from the PDF and restructures them into an internal normalized_images array (accessed via the path
/document/normalized_images/*). The built-in OCR skill strictly requires this normalized image collection format as its input payload.
Incorrect:
[Not C]
Content Field Limitations: The /document/content field generated during document cracking holds only the raw text extracted from the file. It does not contain the binary data or structural properties of embedded images.
Targeting Errors: Pointing an OCR skill directly at the raw text content field would fail to analyze the actual images, leaving the agent unable to ground answers in visual elements like diagrams or embedded infographics.
Reference:
https://docs.azure.cn/en-us/search/tutorial-skillset
NEW QUESTION # 16
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: A
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 # 17
You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
* Some answers are unsupported by retrieved documents.
* A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Unsupported responses: Groundedness evaluation metrics
Policy violations: Risk and safety metrics
For unsupported responses, use Groundedness evaluation metrics . In a Retrieval Augmented Generation scenario, the key question is whether the generated answer is supported by the retrieved context. Microsoft Foundry built-in evaluators define Groundedness as the RAG metric that measures how grounded a response is in retrieved context and returns a model-based score; Groundedness Pro evaluates whether the response is grounded in retrieved context by using Azure AI Content Safety. This directly matches answers that are unsupported by internal documentation.
For policy violations, use Risk and safety metrics . Microsoft Foundry risk and safety evaluators assess generated responses for safety risks such as hate and unfairness, sexual content, violence, self-harm, protected material, indirect attacks, code vulnerability, ungrounded attributes, prohibited actions, and sensitive data leakage. The guidance states that these evaluators assign risk and safety severity or pass/fail outcomes for AI responses and agent behavior.
Latency breakdown traces diagnose performance, not correctness or policy compliance. Token usage analytics diagnose cost and prompt/response size, not unsupported claims or safety violations. Reference topics:
Microsoft Foundry observability, RAG evaluators, groundedness, risk and safety evaluators, and agent quality evaluation.
NEW QUESTION # 18
You need to configure personalized user interactions for Agent1 based on the business requirements. What should you include in the solution?
Answer: A
Explanation:
Agent1 requires memory because the business requirement explicitly states that the agent must retain conversation context and recall relevant information during future interactions. Microsoft Foundry Agent Service memory provides persistent knowledge that can be retained and retrieved across separate conversations, sessions, devices, and workflows. It can store meaningful information extracted from prior interactions, including user preferences, relevant facts, and summarized context, and then retrieve that information when personalizing later responses.
Short-term memory maintains context within the current conversation. Long-term memory supports continuity across future sessions, which directly addresses Contoso's requirement. Memory scopes should be associated with individual user identities so that one customer's stored information cannot be retrieved during another customer's interaction.
Guardrails enforce safety or behavioral restrictions but do not provide conversation recall. Tools enable the agent to access external systems, while instructions define the agent's role and operating boundaries. Neither tools nor instructions persist user-specific information across sessions.
The AI-103 Study Guide maps this requirement to choose appropriate memory, tool, and knowledge integration services and build agents that integrate retrieval, function calling, and conversation memory
.
NEW QUESTION # 19
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