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| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
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NEW QUESTION # 21
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: A
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 # 22
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
Answer: A
Explanation:
To remedy the "invalid project ID" error when calling the Azure Speech to Text REST API within your Microsoft AI Foundry project, you must update the project property in your API request body from a plain string name/ID to the fully qualified Azure Resource URI of the project.
The Speech to Text REST API (v3.0 and later) strictly expects resource links formatted as URIs rather than individual ID strings.
*-> Step 1: Construct the Correct Project URI
You need to pass the project as an absolute object reference. Construct your project property value using the following format:text
https://<your-region>://<your-project-guid>
Use code with caution.Replace <your-region> with your actual Azure Speech resource region (e.g., eastus, westeurope).Replace <your-project-guid> with the actual system-generated Unique Identifier of your project.
Step 2: Retrieve your Project GUID
Step 3: Update your Agent / REST API Request Body
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/azure-ai-speech
NEW QUESTION # 23
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
* Retain user preferences across multiple conversations.
* Enable users to provide contextual grounding by directly uploading documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To retain user preferences across conversations, use: Agent memory that uses persistent storage To enable users to provide contextual grounding during chats, use the: file search tool The correct capability for retaining user preferences is agent memory that uses persistent storage .
Microsoft Foundry Agent Service memory is a managed long-term memory capability that enables continuity across sessions, devices, and workflows. It is specifically intended to let agents retain user preferences, maintain relevant historical context, and personalize responses across separate conversations. Memory stores provide the persistent storage layer, and scope can be used to segment memories for secure user-specific experiences.
The correct capability for contextual grounding from user-uploaded documents is the file search tool .
Microsoft describes file search as the tool that enables Foundry agents to search through documents and retrieve relevant information from outside the base model, including proprietary product information and user- provided documents. The file search workflow supports uploading files, creating a vector store, enabling the tool on the agent, and querying those documents through the agent.
Conversation history alone supports continuity within a conversation, but it is not durable preference memory across multiple conversations. An Azure AI Search tool is better for preconfigured enterprise indexes, while file search is the direct document-upload grounding capability. Reference topics: Foundry Agent Service memory, memory stores, File Search tool, vector stores, and grounded agent responses.
NEW QUESTION # 24
You have an invoice-processing application named App1 that uses Azure Constant Understanding in Foundry Tools.
You are building a new Content Understanding pipeline named Pipeline1 that must meet the following requirements:
- Compare an invoice to its related purchase order
- Validate the voice against static vendor contact documents
- Return a single structured output that includes discrepancy findings
You need to configure Pipeline1 and expose the pipeline as a single analyzer endpoint. What should you configure?
Answer: A
Explanation:
Multiple-file task: Required over a single-file task. Your pipeline needs to reconcile data across two separate active transactional documents (the invoice and the purchase order) within a single analyzer request.
Pro mode is required instead of standard mode. Pro mode is specifically designed for advanced scenarios requiring multi-step reasoning, cross-file analysis, and validation against a knowledge base. Note that Pro mode currently supports classify and generate fields but does not support confidence scores for specific extracted fields.
Vendor contract files as reference data: Required. Because the vendor contracts are static compliance documents, they should be uploaded and treated as the analyzer's background knowledge base (reference data) to guide the validation logic.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/standard-pro-modes
NEW QUESTION # 25
You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers. The results are often incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?
Answer: C
Explanation:
The most appropriate solution is Azure Content Understanding in Foundry Tools.
The Azure Content Understanding service natively combines advanced Optical Character Recognition (OCR), deep layout analysis, and pre-built generative capabilities. It handles varied document structures, multi-page tables, and template-generalizing field extraction without requiring custom machine learning model training. It operates as a low-administration, out-of-the- box solution perfectly aligned with document intelligence needs.
Incorrect:
[Not A]
Azure Language in Foundry Tools: Azure AI Language focuses primarily on unstructured text analytics, sentiment analysis, text summarization, and conversational capabilities. It lacks the built-in document layout analysis, table parsing, and visual OCR capabilities necessary to process complex scanned PDF invoice structures.
[Not C]
Azure Machine Learning model: Building, training, deploying, and managing a custom model in Azure Machine Learning requires significant data science expertise. This approach introduces high administrative overhead, complex infrastructure management, and manual pipeline maintenance, which violates the requirement for low administration.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5706482/azure-document-intelligence-and-content-understand
NEW QUESTION # 26
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