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Microsoft DP-800 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Developing AI-Enabled Database Solutions (DP-800)
Exam Number:DP-800
Exam Format:Case studies, Multiple choice
Passing Score:700/1000 (typical Microsoft passing score; subject to confirmation)
Certificate Validity Period:1 year (Microsoft role-based certifications require annual renewal)
Available Languages:English
Recommended Training:Microsoft Learn - Azure Data & AI Learning Paths
Exam Registration:Microsoft Certification Dashboard
Sample Questions:Microsoft DP-800 Sample Questions
Exam Way:Online proctored or onsite testing center
Pre Condition:No formal prerequisites required, but familiarity with Azure data services and database concepts is recommended
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/

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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 (Q50-Q55):

NEW QUESTION # 50
You have an Azure SQL database that contains database-level Data Definition Language (DDL) triggers, including a trigger named ddl_Audit.
You need to prevent ddl_Audit from firing during the next deployment. The trigger object must remain in place.
Which Transact-SQL statement should you use?

Answer: A

Explanation:
The DISABLE TRIGGER Transact-SQL statement is the correct and appropriate solution for this scenario.
Solution Breakdown
To prevent a specific database-level DDL trigger from firing without removing the object, you can use the following syntax:
DISABLE TRIGGER [TriggerName] ON DATABASE;
Key Considerations
Object Retention: A disabled trigger remains in the database as an object and is visible in catalog views like sys.triggers, but it will not execute when its programmed events occur.
Reactivation: You can re-enable the trigger after your deployment is complete using the ENABLE TRIGGER statement.
Permissions: To execute this command on a database-scoped DDL trigger in Azure SQL, you must have at least ALTER ANY DATABASE DDL TRIGGER permission.
Reference:
https://learn.microsoft.com/en-us/sql/t-sql/statements/disable-trigger-transact-sql


NEW QUESTION # 51
What is a common pattern for integrating LLMs with SQL?

Answer: A

Explanation:
RAG ensures accurate, up-to-date responses using SQL data.


NEW QUESTION # 52
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.

Answer: A,E

Explanation:
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


NEW QUESTION # 53
Hotspot Question
You have an Azure SQL database that contains a table named Table1. Table1 contains
25,000,000 rows of data and a datetime2 column named DateKey. The data in Table1 spans the years 2020 through 2021.
You need to partition the data in Table1 by year. The solution must minimize how long it takes to rebuild or reindex the table.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 54
You have an Azure SQL database That contains a table named dbo.Products, dbo.Products contains three columns named Embedding Category, and Price. The Embedding column is defined as VECTOR(1536).
You use Ai_GENERME_EMBEDOINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3-smalL Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?

Answer: D


NEW QUESTION # 55
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