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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
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NEW QUESTION # 72
You have a Microsoft Foundry project that contains three agents as shown in the following table.
You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
- Support a deterministic, step-based process that uses conditional
branching and shared state across the agents.
- Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?
Answer: C
Explanation:
To fulfill your requirements while keeping development effort to an absolute minimum, you should leverage the native Microsoft Foundry Multi-Agent Workflows feature (built directly into the Foundry Agent Service and managed via the Foundry portal visual editor or declarative YAML files).Using this visual, low-code orchestration layer removes the need to write custom graph routing logic, state managers, or manual handoffs in code.
The minimum required architecture and features that must be included in your solution are structured below.
1. The Orchestration Layer: Declarative Workflow
Instead of writing a code-first orchestrator, you must define a Foundry Workflow Definition (YAML or Visual).
2. State Management: Shared Context Variables
3. Agent Configuration & Native Tooling
Reference:
https://devblogs.microsoft.com/foundry/introducing-multi-agent-workflows-in-foundry-agent-service/
NEW QUESTION # 73
You need to optimize content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Safety metaprompt feature to run the tests.
Does this meet the requirement?
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The Safety metaprompt is not the Content Safety feature intended to measure harm-category severity and tune moderation thresholds against representative sample text.
A safety metaprompt provides additional behavioral instructions intended to guide a generative model toward safer responses. It can complement other safety controls, but it does not perform the specific text moderation test required to determine whether configured severity thresholds accept or reject sample prompts.
For threshold testing, Microsoft provides the Moderate text content experience. The user supplies sample text, selects Run test , and Content Safety returns detected categories and severity values. The Configure filters interface can then be used to change allowed or prohibited severity levels and retest the samples.
Microsoft documents severity classifications including safe, low, medium, and high. Microsoft Learn Therefore, the proposed Safety metaprompt solution does not meet the stated requirement.
This maps to the AI-103 objectives to configure safety filters, guardrails, risk detection, and content moderation , and to apply responsible-AI safety evaluations. Microsoft Learn
NEW QUESTION # 74
You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
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.
NEW QUESTION # 75
In Microsoft Foundry, you use the Chat playground with the GPT-35 Turbo model. You have a prompt that contains the following code.
You need the model to create an explanation of the code. The solution must minimize costs. What should you do?
Answer: C
Explanation:
Add the natural-language comment // what does function F do? after the code. The comment gives GPT-35 Turbo a direct and unambiguous instruction to analyze and explain function F. Because it uses syntax that naturally belongs beside source code, the model can distinguish the program being analyzed from the requested task and generate an explanatory completion. Microsoft's prompt-engineering guidance recommends clearly stating the required outcome and using cues that direct the model toward the desired response.
This approach retains the existing GPT-35 Turbo deployment and adds only a small number of prompt tokens, satisfying the cost-minimization requirement. Changing to GPT-4-32k would be unnecessary because the requirement is a straightforward code-explanation task and does not indicate that a substantially larger context window is required.
Adding function F(explanation) resembles a new function declaration or invocation rather than an instruction to explain existing code. The model could interpret it as source code that must be completed. Setting temperature to 1 changes output randomness but does not tell the model to explain the function; it can also make the answer less consistent.
Study Guide alignment: Use prompt-engineering techniques, construct clear model instructions, tune generation behavior, and select an appropriate model based on quality and cost requirements.
NEW QUESTION # 76
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.
Answer:
Explanation:
Explanation:
* Extract the text: Azure Vision in Foundry Tools
* Identify well-known locations: Azure Language in Foundry Tools
Azure Vision in Foundry Tools is the appropriate service for extracting text from road signs. Its pretrained Optical Character Recognition capability detects printed or handwritten text in images and returns recognized words, lines, and their coordinates. Microsoft specifically identifies street signs as an applicable OCR scenario. Because the OCR model is already trained, it avoids the data labeling and custom-model training that would increase development effort.
After OCR produces the text, Azure Language in Foundry Tools can analyze it through entity linking . Entity linking recognizes and disambiguates well-known entities and connects them to entries in a recognized knowledge base. This is suitable for determining that text on a sign represents a known geographic location rather than merely returning an unclassified text string. Named entity recognition can also classify extracted entities under categories such as Location, but entity linking provides the stronger interpretation implied by
"well-known locations."
Azure Document Intelligence is optimized for structured and text-heavy business documents, while Azure AI Search provides indexing and retrieval rather than primary OCR and linguistic analysis.
Study Guide alignment: implement OCR-based ingestion and use Foundry Tools to extract entities from text .
NEW QUESTION # 77
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