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| Section | Weight | Objectives |
|---|---|---|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
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NEW QUESTION # 60
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search for Retrieval Augmented Generation (RAG).
You plan to ingest and index PDF product manuals.
You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Which indexing approach should you use?
Answer: A
Explanation:
Vector search is the correct indexing and retrieval type to use for this solution.
Vector search versus semantic ranking
While both features help bridge the gap between different wordings, vector search is specifically designed at the indexing and retrieval layer to handle semantic similarity matching by converting text into mathematical vectors (embeddings) based on conceptual meaning. Semantic ranking is a secondary re-ranking layer applied after initial retrieval to improve precision, but it cannot function as the primary indexing method on its own.
Reference:
https://www.scribd.com/document/866453317/Agentforce-Specialist
NEW QUESTION # 61
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 # 62
You need to add an automated grounding check to a RAG application's continuous evaluation.
The check must return a simple pass or fail result and must not require you to deploy a separate judge model. Which evaluator should you use?
Answer: C
Explanation:
Groundedness Pro returns a binary pass or fail result and runs on the Azure AI Content Safety service, so it does not require you to deploy a model to act as a judge. That matches both requirements in the scenario.
NEW QUESTION # 63
Hotspot Question
You have a Microsoft Foundry project.
- You need to create a customer support agent that meets the following
requirements:
- Grounds responses only in company policy documents stored in curated
repositories
- Retains customer preferences across separate chat sessions
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Configure retrieval from approved data sources
You should configure retrieval from approved data sources to meet this requirement.
In Microsoft Foundry (and broader Azure AI Foundry / Azure OpenAI architectures), grounding an agent exclusively in company policy documents requires implementing a Retrieval-Augmented Generation (RAG) pattern. This ensures the AI model only answers using the provided context and does not hallucinate or rely on its public training data.
Box 2: Enable agent memory that uses persistent storage
To retain user preferences across completely separate chat sessions or conversations, you must use persistent agent memory. Traditional chat history only tracks messages within a single active thread or session. By contrast, the Memory Store feature provides a managed, long-term memory system that structurally indexes user preferences and facts across different devices and separate sessions.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-capabilities-for-azure-openai-on-your-data/4144636
NEW QUESTION # 64
You have a Microsoft Foundry project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
* Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI Search.
* Require deep, multi-step reasoning across long contexts.
* Generate detailed natural language responses.
Which type of model should you use to power the agent?
Answer: D
Explanation:
The correct model type is a large language model (LLM) . The scenario requires an agent that can reason over retrieved documentation, synthesize context-aware answers, and generate detailed natural language responses. Microsoft Foundry RAG guidance defines Retrieval Augmented Generation as a pattern that combines search with large language models so responses are grounded in organizational data, which directly matches the use of Azure AI Search for internal product documentation.
An LLM is also the correct fit for deep, multi-step reasoning across long contexts. Azure AI Search agentic retrieval guidance states that complex chat and agent scenarios use an LLM to break a user query into smaller focused subqueries for better coverage over indexed content. Microsoft also describes Azure AI Search as a way to ground agents and chatbots in proprietary enterprise data for accurate, context-aware responses.
A multimodal model is unnecessary because the requirements are text-focused, not image, audio, or video based. A key phrase extraction model performs narrow text analytics and cannot generate detailed answers.
An SLM may reduce cost and latency, but the stated need for deep reasoning and long-context synthesis favors an LLM. Reference topics: Microsoft Foundry RAG, Azure AI Search grounding, agentic retrieval, and model selection for generative AI agents.
NEW QUESTION # 65
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