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| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
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NEW QUESTION # 152
You have an agent named Agent1 that uses Model Context Protocol (MCP) calls to retrieve external data.
You need to implement guardrails to ensure that Agent1 cannot send any content tagged as Violence to the MCP server. Which intervention point should you use?
Answer: B
Explanation:
The correct intervention point is tool call because the prohibited content must be inspected before Agent1 transmits the MCP request to the external server. Microsoft Foundry defines a tool call as "the action and data the agent proposes to send to a tool." This includes the selected MCP operation and its arguments or payload.
A guardrail configured at this stage evaluates the outbound content immediately before execution. When the Violence risk is detected and the response action is configured as Annotate and block , the tool invocation is not executed, preventing the restricted data from leaving the agent boundary.
The other intervention points operate at different stages. User input scans the original user prompt, but harmful content could subsequently be generated within the tool arguments. Tool response scans information returned by the MCP server, which occurs after the outbound content has already been transmitted. Output scans the final answer delivered to the user and therefore does not control agent-to-tool communication.
This aligns with the AI-103 Study Guide topics Configure safety filters, guardrails, risk detection, and content moderation and Govern agent behavior with oversight modes, constraints, and tool-access controls under responsible AI and agentic systems.
NEW QUESTION # 153
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
* Ensure that the agent can perform calculations during conversations
* Ensure that the agent can access up-to-date information from public websites.
* Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool 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:
Access up-to-date information from public websites: Grounding with Bing Search Perform calculations during conversations: Code interpreter Retrieve information from documents uploaded directly to the agent: File search The correct tool for public, current web information is Grounding with Bing Search . Microsoft Foundry Agent Service identifies Grounding with Bing Search as the built-in tool that enables an agent to access and return information from the internet, which fits the requirement for up-to-date public website data. ( learn.
microsoft.com )
For calculations during conversations, use Code interpreter . Microsoft's Foundry guidance states that Code Interpreter enables an agent to run Python code in a sandboxed execution environment and solve data analysis and math tasks iteratively. This is the correct fit for financial analysts who need calculations, analysis, and potentially chart generation during the conversation.
For documents uploaded directly to the agent, use File search . Microsoft describes File Search as the tool that enables Foundry agents to search through documents, retrieve relevant information, and augment model responses with knowledge from uploaded files such as PDFs, Word documents, and proprietary content.
Computer use is for interacting with graphical applications, not calculation or document retrieval. Microsoft Fabric is for enterprise data and analytics integration, not direct uploaded document retrieval. Reference topics: Foundry Agent Service tools, Code Interpreter, File Search, and Grounding with Bing Search.
NEW QUESTION # 154
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 # 155
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
Answer: C
Explanation:
To prevent potentially malicious instructions embedded within untrusted external sources (such as text extracted from uploaded images via OCR) from being processed by the model, you should use Prompt Shields for Documents (specifically designed for Indirect Prompt Attacks or Cross- Domain Prompt Injections).
Key Features to Implement
Prompt Shields for Documents: This specific component of Azure AI Content Safety / Prompt Shields analyzes external data--like text pulled from files, emails, or OCR outputs--appended to the prompt. It identifies and neutralizes hidden adversarial instructions trying to trick the model into overriding its system protocols.
Spotlighting: A native capability within Microsoft Foundry's Prompt Shields, Spotlighting uses advanced text-formatting techniques to separate valid user commands from the untrusted document text. This prevents the model from mistaking malicious instructions embedded inside the OCR output for instructions from the developer or end-user.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/azure-ai-announces-prompt-shields-ga/4236033
NEW QUESTION # 156
You have a Microsoft Foundry project that contains an agent.
The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes.
The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables.
You plan to call Azure Content Understanding in Foundry Tools.
You need to extract content and layout elements and detect QR codes without requiring a language model deployment.
Which built-in analyzer should you use?
Answer: C
Explanation:
To extract content, preserve tables and document layout, and detect embedded QR codes without deploying a large language model (LLM), you should use the built-in prebuilt-layout analyzer.
Note:
Unlike schema-driven extraction models in Content Understanding that utilize generative AI orchestration, the Layout analyzer is a highly efficient machine-learning-based model. It natively outputs structural geometry and decodes barcodes without requiring an active LLM deployment or provisioned throughput.
Structural Preservation: It extracts headers, paragraphs, and nested sections, returning precise spatial bounding boxes for every single element. Downstream agents can utilize this geometric metadata to anchor or cross-reference sections accurately.
Advanced Table Mapping: It maps intricate, multi-page invoice tables, capturing text alongside row and column indices. You can configure the output structure format natively into Markdown or HTML tables to maintain formatting cleanliness.
Built-in QR and Barcode Decoding: By default, the configuration parameter enableBarcode is set to true. The analyzer scans the scanned PDF image, isolates 2D code regions, and appends the decoded string payload into the output JSON alongside text blocks.
Zero LLM Dependency: It does not route text to foundational models like GPT-4o for its extraction, keeping processing latency low and lowering operational costs significantly.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/content-understanding-studio
NEW QUESTION # 157
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