저희는 수많은 IT자격증시험에 도전해보려 하는 IT인사들께 편리를 가져다 드리기 위해 Microsoft DP-800실제시험 출제유형에 근거하여 가장 퍼펙트한 시험공부가이드를 출시하였습니다. 많은 사이트에서 판매하고 있는 시험자료보다 출중한Fast2test의 Microsoft DP-800덤프는 실제시험의 거의 모든 문제를 적중하여 고득점으로 시험에서 한방에 패스하도록 해드립니다. Microsoft DP-800시험은Fast2test제품으로 간편하게 도전해보시면 후회없을 것입니다.
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Fast2test의 Microsoft인증 DP-800시험덤프는 실제시험의 기출문제와 예상문제를 묶어둔 공부자료로서 시험문제커버율이 상당히 높습니다.IT업계에 계속 종사하려는 IT인사들은 부단히 유력한 자격증을 취득하고 자신의 자리를 보존해야 합니다. Fast2test의 Microsoft인증 DP-800시험덤프로 어려운 Microsoft인증 DP-800시험을 쉽게 패스해보세요. IT자격증 취득이 여느때보다 여느일보다 쉬워져 자격증을 많이 따는 꿈을 실현해드립니다.
질문 # 28
You have an Azure SQL database that contains a table named knowledgebase, knowledgebase stores human resources (HR) policy documents and contains columns named title, content, category, and embedding.
You have an application named App1. App1 queries two relational tables named employee_pnofiles and benefits_enrollnent that contain HR data. App1 hosts a chatbot that calls a large language model (LLM) directly.
Users report that the chatbot answers general HR questions correctly but provides outdated or incorrect answers when policies change. The chatbot also fails to answer questions that reference internal policy documents by title or category.
You need to recommend a Retrieval Augmented Generation (RAG) solution to resolve the chatbot issues.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
The correct recommendation is to retrieve grounding data from knowledge_base and, at inference time, generate query embeddings and run a vector similarity search .
The chatbot currently answers some general HR questions but fails when policies change and when users ask about internal policy documents by title or category . That is exactly the kind of problem RAG is meant to solve: ground the LLM in the organization's proprietary content instead of relying on the model's training data or unrelated transactional tables. Microsoft's RAG guidance states that RAG extends LLMs by grounding responses in your own content and that, for agentic retrieval, knowledge bases unify knowledge sources for retrieval.
So the grounding data should come from knowledge_base , because that table stores the HR policy documents and already includes fields like title, content, category, and embedding. Those are the fields directly tied to the missing and outdated policy answers. By contrast:
* employee_profiles and benefits_enrollment are operational HR tables, not the authoritative store for policy-document grounding.
* PDF exports of the policies would be inferior to querying the indexed/structured knowledge base already prepared for retrieval.
* The LLM training data is specifically the wrong source when the issue is outdated internal content.
For the retrieval step, Microsoft's guidance says to use embeddings for vector queries and notes that vector similarity search matches concepts, not exact terms . This is especially important because users ask about policy documents by title or category and also phrase questions in ways that might not exactly match document wording. Generating a query embedding and then running a vector similarity search is the appropriate retrieval step in a RAG pipeline.
질문 # 29
You have a SQL database in Microsoft Fabric that contains a column named Payload. Payload stores customer data in JSON documents that have the following format:
JSON
{
" date " : " 2026-01-25 " ,
" customer_email " : " user@contoso.com " ,
...
}
Data analysis shows that some customers have subaddressing in their email address; for example, user1+promo@contoso.com.
You need to return a normalized email value that removes the subaddressing, for example, user1+promo@contoso.com must be normalized to user1@contoso.com.
Which Transact-SQL expression should you use?
정답：D
질문 # 30
What is Retrieval-Augmented Generation (RAG)?
정답：A
질문 # 31
You have an Azure SQL database that contains a table named dbo.ManualChunks. dbo.HonualChunks contains product manuals A retrieval query already returns the top five matching chunks as nvarchar(max) text.
You need to call an Azure OpenAI REST endpomt for chat completions. The request body must include both the user question and theretiieved chunks.
You write the following Transact-SQL code.
What should you insert at line 22?
정답：C
설명：
The correct insertion at line 22 is FOR JSON PATH, WITHOUT_ARRAY_WRAPPER .
The request body for the Azure OpenAI chat completions call must be a single JSON object containing the messages array with both the system/user content and the retrieved chunks. Microsoft documents that FOR JSON PATH is the preferred way to shape JSON output, especially when you want precise control over nested property names like messages[0].role and messages[1] .content.
The key detail is WITHOUT_ARRAY_WRAPPER . By default, FOR JSON returns results enclosed in square brackets as a JSON array. Microsoft documents that WITHOUT_ARRAY_WRAPPER removes those brackets so a single JSON object is produced instead. That is exactly what is needed here for @payload, because the stored procedure is building one request body, not an array of request bodies.
질문 # 32
Hotspot Question
You have an Azure AI Search service and an index named hotels that includes a vector field named DescriptionVector.
You query hotels by using the Search Documents REST API.
You add semantic ranking to the hybrid search query and discover that some queries return fewer results than expected, and captions and answers are missing.
You need to complete the hybrid search request to meet the following requirements:
- Include more documents when ranking.
- Always include captions and answers.
How should you complete the REST request body? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 33
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Fast2test의 인지도는 고객님께서 상상하는것보다 훨씬 높습니다.많은 분들이Fast2test의 덤프공부가이드로 IT자격증 취득의 꿈을 이루었습니다. Fast2test에서 출시한 Microsoft인증 DP-800덤프는 IT인사들이 자격증 취득의 험난한 길에서 없어서는 안될중요한 존재입니다. Fast2test의 Microsoft인증 DP-800덤프를 한번 믿고 가보세요.시험불합격시 덤프비용은 환불해드리니 밑져봐야 본전 아니겠습니까?
DP-800최고덤프문제: https://kr.fast2test.com/DP-800-premium-file.html