DP-800인증시험, DP-800최신시험대비공부자료

Pass4Test에서 출시한 Microsoft인증 DP-800덤프는Microsoft인증 DP-800시험에 대비하여 IT전문가들이 제작한 최신버전 공부자료로서 시험패스율이 100%입니다.Pass4Test는 고품질 Microsoft인증 DP-800덤프를 가장 친근한 가격으로 미래의 IT전문가들께 제공해드립니다. Pass4Test의 소원대로 멋진 IT전문가도 거듭나세요.

Microsoft DP-800 시험요강:

주제소개
주제 1
  • Secure, optimize, and deploy database solutions: This domain focuses on implementing data security measures like encryption, masking, and row-level security, optimizing query performance, managing CI
  • CD pipelines using SQL Database Projects, and integrating SQL solutions with Azure services including Data API builder and monitoring tools.
주제 2
  • Design and develop database solutions: This domain covers designing and building database objects such as tables, views, functions, stored procedures, and triggers, along with writing advanced T-SQL code and leveraging AI-assisted tools like GitHub Copilot and MCP for SQL development.
주제 3
  • Implement AI capabilities in database solutions: This domain covers designing and managing external AI models and embeddings, implementing full-text, semantic vector, and hybrid search strategies, and building retrieval-augmented generation (RAG) solutions that connect database outputs with language models.

>> DP-800인증시험 <<

시험대비 DP-800인증시험 최신 덤프문제

Pass4Test에서 출시한 Microsoft 인증 DP-800시험덤프는Pass4Test의 엘리트한 IT전문가들이 IT인증실제시험문제를 연구하여 제작한 최신버전 덤프입니다. 덤프는 실제시험의 모든 범위를 커버하고 있어 시험통과율이 거의 100%에 달합니다. 제일 빠른 시간내에 덤프에 있는 문제만 잘 이해하고 기억하신다면 시험패스는 문제없습니다.

최신 Microsoft Certified: SQL AI Developer DP-800 무료샘플문제 (Q74-Q79):

질문 # 74
You need to recommend a solution that will resolve the ingestion pipeline failure issues. Which two actions should you recommend? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

정답：A,D

설명：
The two correct actions are D and E because the ingestion failures are caused by malformed JSON and duplicate payloads, and these two controls address those two problems directly. Microsoft's JSON documentation states that SQL Server and Azure SQL support validating JSON with ISJSON, and Microsoft specifically recommends using a CHECK constraint to ensure JSON text stored in a column is properly formatted.
For the duplicate-payload issue, creating a unique index on a hash of the payload is the appropriate design.
Microsoft documents using hashing functions such as HASHBYTES to hash column values, and SQL Server allows a deterministic computed column to be used as a key column in a UNIQUE constraint or unique index.
That makes a persisted hash-based computed column plus a unique index a practical and exam-consistent way to reject duplicate payloads efficiently.
The other options do not solve the stated root causes:
Snapshot isolation addresses concurrency behavior, not malformed JSON or duplicate payload detection.
A trigger to rewrite malformed JSON is not the right integrity control and is brittle.
Foreign key constraints enforce referential integrity, not JSON validity or duplicate-payload prevention


질문 # 75
You need to meet the development requirements for the FeedbackJson column How should you complete the Transact SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:
JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText
CONTAINS(FeedbackJson, @Keyword)
SimilarityScore
These three selections are the correct way to complete the query because they align exactly with the stated requirements for the FeedbackJson column.
First, to extract the customer feedback text from the JSON document , the correct expression is JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText . Microsoft documents that JSON_VALUE is used to extract a scalar value from JSON, while JSON_QUERY is used for returning an object or array .
Since $.text is the textual feedback string, JSON_VALUE is the correct function.
Second, to filter rows where the JSON text contains a keyword , the best choice is CONTAINS (FeedbackJson, @Keyword) . The scenario explicitly states that FeedbackJson already has a full-text index
, and Microsoft documents that CONTAINS is the full-text predicate used in the WHERE clause to search full-text indexed character data. That makes it more appropriate than using EDIT_DISTANCE for keyword filtering.
Third, to order the results by similarity score, highest first , the correct item is SimilarityScore in the ORDER BY clause, which would be paired with DESC in the query. This matches the requirement to sort by the computed fuzzy similarity value. The DP-800 study guide specifically includes writing queries that use fuzzy string matching functions such as EDIT_DISTANCE, which supports the earlier computed SimilarityScore expression in the query.


질문 # 76
You have an Azure SQL database that stores order data.
A reporting query aggregates monthly revenue per customer runs frequently.
You need to reduce how long it takes to retrieve the calculated values. The solution must NOT alter any underlying table structure.
What should you do?

정답：A

설명：
Creating an indexed view using WITH SCHEMABINDING, including COUNT_BIG(*), and creating a unique clustered index is a valid and effective approach to significantly improve the performance of your frequent reporting query without altering the underlying table structure.
This process materializes the aggregated data and stores it physically in the database, so the query optimizer can read from the precomputed view rather than rescanning the base tables every time the query runs.
Reference:
https://learn.microsoft.com/en-us/sql/relational-databases/views/create-indexed-views


질문 # 77
You have an Azure SQL database that contains tables named dbo.Tickets and dbo.TicketNotes.
dbo.Tickets contains support tickets and dbo.TicketNotes contains ticket notes.
A retrieval query returns the top five relevant ticket notes for a user question.
You plan to implement a Retrieval Augmented Generation (RAG) pattern that meets the following requirements:
- Formats the retrieved relational data for large language model (LLM)
processing
- Sends the user question and retrieved context to an Azure OpenAI REST endpoint for chat completions
- Extracts the response text from the LLM response
Which Transact-SQL function should you use to extract the response text?

정답：C

설명：
The Transact-SQL function used to extract the response text from the Azure OpenAI REST endpoint is JSON_VALUE.
When you call the Azure OpenAI REST endpoint using sp_invoke_external_rest_endpoint, the response is returned as a JSON string. To isolate the actual text content from the LLM, you must parse this JSON structure.
Function: JSON_VALUE(response_body, '$.choices[0].message.content')
Purpose: It extracts a scalar (text) value from a JSON string.
Path: In the OpenAI schema, the generated response is always located at
$.choices[0].message.content.
Reference:
https://pub.towardsai.net/mastering-retrieval-augmented-generation-from-zero-to-expert-in-rag- for-quickly-building-a-08141a308836


질문 # 78
You have an Azure container app named app1-contoso-001 that hosts a Data API builder (DAB) container in front of an Azure SQL database.
You add an entity named Todo that uses a source named dbo.todos.
Which URL pattern should clients use to access the Todo REST endpoint?

정답：C

설명：
To access the REST endpoint for the Todo entity, clients should use the following URL pattern:
https://<your-container-app-url>/api/Todo
Base URL: The unique FQDN of your Azure Container App.
Path Prefix: By default, DAB uses /api for RESTful services.
Entity Name: You must use the Entity Name defined in the configuration (Todo), not the underlying source name (dbo.Todos).
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/connect-apps


질문 # 79
......

Pass4Test의Microsoft인증 DP-800시험덤프공부가이드 마련은 현명한 선택입니다. Microsoft인증 DP-800덤프구매로 시험패스가 쉬워지고 자격증 취득율이 제고되어 공을 많이 들이지 않고서도 성공을 달콤한 열매를 맛볼수 있습니다.

DP-800최신 시험대비 공부자료: https://www.pass4test.net/DP-800.html