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Microsoft DP-800 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI-Enabled Database Solutions
Exam Number:DP-800
Available Languages:German, Indonesian (Indonesia), Chinese (Traditional), Chinese (Simplified), Arabic (Saudi Arabia), English, Korean, Japanese, Italian, Spanish, French, Portuguese (Brazil)
Passing Score:700
Real Exam Qty:40-60
Exam Duration:100 minutes
Related Certifications:Microsoft Certified: SQL AI Developer Associate
Exam Price:$165 USD
Certificate Validity Period:1 year
Exam Format:Build list, Multiple select, Drag and drop, Multiple choice, Case studies
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
  • 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.
Topic 3
  • 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.

Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q79-Q84):

NEW QUESTION # 79
You have an Azure SQL database that contains a table named Tickets_Embeddings. Tickets_Embeddings contains a VECTOR(1536) column.
Embeddings are generated by using text-embedding-ada-002 and the /openai/v1/embeddings endpoint.
After a review of the current environment, the following changes are requested:
The AI architect has recommended switching to text-embedding-3-small.
The security team requires authentication by using Microsoft Entra managed identities only. Storing API keys in an application is prohibited.
Which two actions should you include in the solution? Each correct answer presents a part of the solution.
NOTE: Each correct selection is worth one point.

Answer: C,E


NEW QUESTION # 80
You need to design a generative Al solution that uses a Microsoft SOL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
* Ait ' grounded In the latest transactional and reference data stored in D61
* Do NOT require retraining or fine-tuning the language model when the data changes
* Can include citations or references to the source data used in the response Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern?
More than one answer choice may achieve the goal. Select the BEST answer

Answer: A

Explanation:
The best use case for RAG is answering user questions based on company-specific knowledge . Microsoft defines RAG as a pattern that augments a language model with a retrieval system that provides grounding data at inference time, which is exactly what you need when responses must be based on the latest transactional and reference data , must avoid retraining/fine-tuning , and should be able to include citations or references to source data.
The other options do not fit as well:
* summarizing free-form user input does not inherently require retrieval from DB1,
* training a custom model contradicts the requirement to avoid retraining/fine-tuning,
* generating marketing slogans is a creative generation task, not a grounding-and-citation scenario. RAG is specifically strong when answers must come from your organization's own changing knowledge.


NEW QUESTION # 81
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.

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?