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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 3: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 4: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 5: Implement agentic solutions | 20-25% | - Build AI agents
|
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NEW QUESTION # 114
Drag and Drop Question
You have a Microsoft Foundry project that processes procurement documents submitted by suppliers.
You need to implement two pipelines by using Azure Content Understanding in Foundry Tools.
The solution must meet the following requirements:
- Include a pipeline named Pipeline1 that supports cost-effective,
high-volume processing of standalone PDF invoices.
- Include a pipeline named Pipeline2 that supports cross-document
validation by using multi-step reasoning and reference data.
How should you configure each pipeline? To answer, drag the appropriate configurations to the correct pipelines. Each configuration may be used once, more than once, of 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:
NEW QUESTION # 115
You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?
Answer: D
Explanation:
To identify whether model input, model output, or expanded tool usage is driving the cost spike, you should focus on token usage.
Granular tracking of token usage breaks down the costs directly:
Input vs. Output Tokens: Tracing token usage isolates whether the user prompt (input) has grown or if the model is generating longer, more expensive responses (output).
Tool Calling: Expanded tool usage consumes extra tokens each time the agent schema processes an execution loop, which will clearly reflect as inflated token counts.
Reference:
https://www.mindstudio.ai/blog/subtraction-principle-agent-harness-optimization
NEW QUESTION # 116
You need to recommend an invoice review solution that resolves the issue reported by the finance department.
What should you include in the recommendation?
Answer: D
Explanation:
The correct recommendation is Azure Content Understanding in Foundry Tools . The case study states that Contoso's finance department must manually review vendor invoices to verify that invoice details match vendor contract terms, and that the invoices contain tables, logos, and varied layouts that make consistent processing difficult. It also states that the planned solution must evaluate both the visual layout and textual content of the invoices.
Azure Content Understanding is designed for this type of multimodal document-processing workload.
Microsoft describes Content Understanding as a Foundry Tool that processes unstructured and multimodal content, including documents and images, and transforms it into structured output for AI applications. It can use document analyzers to extract text, layout, tables, fields, and relationships from diverse document types.
Chat completions alone would not reliably extract structured invoice fields from complex layouts. Azure Document Intelligence can extract OCR, layout, and tables, but Content Understanding is the better end-to- end Foundry capability for combining visual and textual understanding with structured extraction for downstream verification. Image Analysis focuses on image-level visual features and is insufficient for invoice field and table review. Reference topics: Content Understanding, document analyzers, multimodal extraction, invoice processing, tables, layout, and structured JSON output.
NEW QUESTION # 117
You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?
Answer: B
Explanation:
Setting the output format parameter to Markdown is the correct and recommended action, but the exact property and enum name depend on whether you are interacting with the REST API directly or using the Python SDK.
To process PDF invoices and preserve their tables, headings, and visual sections in GitHub Flavored Markdown (GFM), configure your Azure Document Intelligence layout model parameters using the precise syntax detailed below.
Implementation Details
Python SDK Syntax: In the Azure Python client library, the parameter name is output_content_format, and its required value is DocumentContentFormat.MARKDOWN (rather than ContentFormat.MARKDOWN, which is used in the .NET C# SDK) Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/markdown-elements
NEW QUESTION # 118
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
- Each workflow run must include a retrieval step before generating a
response.
- Tool calls must authenticate by using the published agent's own
identity.
- Tool access must use an identity isolated from other project
resources.
- Tool access must use support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 119
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