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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q35-Q40):

NEW QUESTION # 35
You need to recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements.
What should you include in the recommendation?

  • A. Add the schema to a GitHub Copilot instruction file.
  • B. Export the database schema as a .dacpac file and load the schema into a GitHub Copilot context window.
  • C. Include the database project in the code repository.
  • D. Use an MCP server

Answer: D

Explanation:
The best recommendation is to use an MCP server . In the official DP-800 study guide , Microsoft explicitly lists skills such as configuring Model Context Protocol (MCP) tool options in a GitHub Copilot session and connecting to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse . That makes MCP the exam-aligned mechanism for enabling AI-assisted tools to work with live database context rather than static snapshots.
This also matches the stated development requirement: the team will use Visual Studio Code and GitHub Copilot and needs to retrieve live metadata from the databases . Microsoft's documentation for GitHub Copilot with the MSSQL extension explains that Copilot works with an active database connection , provides schema-aware suggestions , supports chatting with a connected database, and adapts responses based on the current database context . Microsoft also documents MCP as the standard way for AI tools to connect to external systems and data sources through discoverable tools and endpoints.
The other options do not satisfy the "live metadata" requirement as well:
* A .dacpac is a point-in-time schema artifact, not live metadata.
* A Copilot instruction file provides guidance, not live database discovery.
* Including the database project in the repository helps source control and deployment, but it still does not provide live database metadata by itself.


NEW QUESTION # 36
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

  • A. answering user questions based on company-specific knowledge
  • B. training a custom language model on historical database data
  • C. summarizing free-form user input text
  • D. generating marketing slogans based on user sentiment analysis

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 # 37
You have a Microsoft SQL Servei 2025 database that contains a table named dbo.Customer-Messages, dbo.
Customer-Messages contains two columns named HessagelD (int) and MessageRaw (nvarchar(iux)).
MessageRaw can contain a phone number in multiple formats. and some rows do NOT contain a phone number. You need to write a single SELECT query that meets the following requirements:
* The query must return Message ID, RawNumber. DigitsOnly, and PhoneStatus.
* RawNumber must contain the first substring that matches a phone-number pattern, or NULL if no match exists.
* DigitsOnly must remove all non-digit characters from RawNumber. or return NULL.
* PhoneStatus must return Valid when a phone number exists in MessageRaw. otherwise return Missing.
How should you complete the Transact-SQL query? lo answer, drag the appropriate values To the correct targets. Each value may be used once, more 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 drag-and-drop mapping is based on the documented behavior of the new SQL regular expression functions.
For RawNumber , the requirement is to return the first substring in MessageRaw that matches the phone- number pattern, or NULL if nothing matches. That is exactly what REGEXP_SUBSTR does: it extracts the matched substring from the source text. Microsoft documents REGEXP_SUBSTR as the function that
"extracts parts of a string based on a regular expression pattern" and returns the matched occurrence.
For DigitsOnly , you first need the matched phone substring, then remove all non-digit characters from it. The correct combined expression is REGEXP_REPLACE( REGEXP_SUBSTR( so the matched substring is passed into REGEXP_REPLACE, which strips characters matching \D. Microsoft documents REGEXP_REPLACE as returning a modified source string with matching patterns replaced. Using it around REGEXP_SUBSTR satisfies the "digits only or NULL" requirement in one select expression.
For PhoneStatus , the requirement is simply to return Valid when a phone number exists and Missing otherwise. That is a Boolean test, so REGEXP_LIKE is the right function. Microsoft documents REGEXP_LIKE as returning a Boolean value indicating whether the input matches the regex pattern.


NEW QUESTION # 38
You have an Azure SQL table that contains the following data.

You need to retrieve data to be used as context for a large language model (LLM). The solution must minimize token usage.
Which formal should you use to send the data to the LLM?

  • A.
  • B.
  • C.
  • D.

Answer: B

Explanation:
The correct choice is Option A because it provides the relevant semantic context the LLM needs while avoiding an unnecessary field that would add tokens without improving answer quality.
For LLM grounding and RAG-style context, Microsoft guidance emphasizes mapping and sending the fields that contain text pertinent to the use case . In this FAQ scenario, the useful context is the ProductName , the Question , and the Answer . Those three fields help the model understand both the subject domain and the actual Q & A pair. By contrast, FaqId is just a technical identifier and generally adds no semantic value for response generation, so including it wastes tokens.
That is why Option A is better than the others:
* Option A keeps the meaningful text fields and removes the low-value identifier.
* Option B is too minimal because it includes only the answer text as Prompt, which strips away the product and question context the LLM may need for accurate grounding.
* Option C keeps FaqId but omits ProductName, which can be important disambiguating context.
* Option D includes everything, but that does not minimize token usage because it keeps the unnecessary FaqId.


NEW QUESTION # 39
You have a database named db1. The schema is stored in a Git repository as an SDK-style SQL database project The repository Contains the following GitHub Action workflow.

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:
* Unit tests run automatically whenever changes are pushed to main. # Yes
* Schema validation occurs during the Build step. # Yes
* Schema validation occurs during the Deploy step. # No
The first statement is Yes . The workflow is configured to trigger on both push to main and pull_request targeting main. The unit-tests job has this condition:
if: github.ref == ' refs/heads/main '
On a push to main , GitHub sets github.ref to refs/heads/main, so the condition is true and the unit-tests job runs. GitHub's workflow syntax documentation confirms that push.branches: [main] triggers on pushes to main, and the github.ref value for branch pushes is the fully qualified ref such as refs/heads/main.
The second statement is Yes . The Build step runs:
dotnet build db1.sqlproj --configuration Release
For an SDK-style SQL database project, the build process produces a .dacpac and validates the database project model as part of compilation/build. Microsoft's SQL database project documentation describes SDK- style SQL projects as the project format used for SQL Database Projects, and Microsoft's command-line build documentation is specifically about building a .dacpac from that SQL project. That means schema-level project validation happens during build.
The third statement is No . The Deploy step uses:
SqlPackage /Action:Publish ...
Microsoft documents that SqlPackage Publish incrementally updates the target database schema to match the source .dacpac. That is a deployment operation, not the primary schema-validation stage of the SQL project source itself. In this workflow, the schema is validated when the SQL project is built into the .dacpac; the deploy step applies that built artifact to the target database.


NEW QUESTION # 40
......

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