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Microsoft DP-800 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI-Enabled Database Solutions
Exam Number:DP-800
Exam Format:Build list, Case studies, Multiple select, Drag and drop, Multiple choice
Related Certifications:Microsoft Certified: SQL AI Developer Associate
Passing Score:700
Certificate Validity Period:1 year
Real Exam Qty:40-60
Available Languages:Spanish, English, Arabic (Saudi Arabia), German, Indonesian (Indonesia), Chinese (Simplified), Korean, Chinese (Traditional), Japanese, Portuguese (Brazil), French, Italian
Exam Duration:100 minutes
Exam Price:$165 USD
Sample Questions:Microsoft DP-800 Sample Questions
Exam Way:Online proctored or in-person at a testing center
Pre Condition:No specific prerequisites, but candidates should have experience with SQL and a basic understanding of Azure services. Familiarity with AI or machine learning concepts is recommended.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-800/

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Microsoft DP-800 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.

Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are developing an Azure SQL solution by using Microsoft Visual Studio 2026. The solution uses a GitHub repository.
You plan to use GitHub Copilot Chat to access the GitHub repository tools by connecting to the GitHub MCP Server.
You need to configure Visual Studio to support the planned configuration. The solution must rely on OAuth to access the MCP server.
What should you create?

Answer: D


NEW QUESTION # 41
You are creating a table that will store customer profiles.
You have the following Transact-SQL code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection Is worth one point.

Answer:

Explanation:

Explanation:
The schema meets the security requirements for PII data. # Yes
Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No. Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query. If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.


NEW QUESTION # 42
What is the role of Azure Synapse in AI + SQL solutions?

Answer: A

Explanation:
Azure Synapse Analytics combines big data and data warehousing for AI-driven insights.


NEW QUESTION # 43
You are developing an Azure SQL database solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
You need to ensure that GitHub Copilot Chat can call the hosted GitHub MCP Server tools by using OAuth.
The MCP server configuration must be scoped to the repository.
What should you do in Visual Studio Code?

Answer: D


NEW QUESTION # 44
You have an Azure SQL database named DB1 that contains two tables named knowledgebase and query_cache. knowledge_base contains support articles and embeddings. query_cache contains chat questions, responses, and embeddings DB1 supports an Al-enabled chat agent.
You need to design a solution that meets the following requirements:
* Serializes the retrieved rows from knowledee_base
* Extracts the answer field from the response
* Extracts the embeddings to store in query_cache
You will call the external large language model (LLM) by using the sp_irwoke_external_re standpoint stored procedure.
Which Transact-SGL commands should you use for each requirement? To answer, drag the appropriate commands to the correct requirements. Each command may be used once, mote than once, or 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:

Explanation:

The correct mapping is:
FOR JSON PATH
JSON_VALUE
JSON_QUERY
To serialize the retrieved rows from knowledge_base, the correct command is FOR JSON PATH. Microsoft documents that FOR JSON formats query results as JSON, and PATH mode is the standard way to shape relational rows into JSON for downstream application or AI use.
To extract the answer field from the response, the correct command is JSON_VALUE because answer is a single scalar field. Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.
To extract the embeddings to store in query_cache, the correct command is JSON_QUERY because embeddings are returned as a JSON array, not a scalar. Microsoft states that JSON_QUERY extracts an object or array from JSON text, which is exactly the right behavior for an embeddings payload.
The unused options are not the best fit here:
OPENJSON is mainly for shredding JSON into rows and columns.
AI_GENERATE_CHUNKS is for chunking text, not extracting fields from a response payload.
VECTOR_DISTANCE computes similarity between vectors and is unrelated to JSON extraction.
FOR XML PATH produces XML, not JSON.


NEW QUESTION # 45
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