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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, optimize, and deploy database solutions | 35-40% | - Optimize performance and reliability
|
| Topic 2: Design and develop database solutions | 35-40% | - Develop database solutions
|
| Topic 3: Implement AI capabilities in database solutions | 25-30% | - Use AI-assisted development tools
|
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NEW QUESTION # 52
Which scenario best fits AI-enhanced SQL querying?
Answer: B
Explanation:
AI allows conversational querying of SQL databases.
NEW QUESTION # 53
You have a SQL database in Microsoft Fabric named Sales BD that contains a table named dbo.Products. You need to modify SalesBD to meet the following requirements:
* Create a vector index on the appropriate column.
* Use a supplied natural language query vector.
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:
Explanation:
The first correct selection is embedding because a vector index must be created on the vector column , not on a scalar distance column or a text column such as product_name. Microsoft's CREATE VECTOR INDEX documentation shows that the index is created directly on the vector-valued column, for example ON product_embeddings(embedding).
The second correct selection is VECTOR_SEARCH because the requirement is to use a supplied natural language query vector and search against the indexed embeddings. Microsoft documents that VECTOR_SEARCH is the Transact-SQL function for approximate nearest neighbor vector retrieval and that it applies to SQL database in Microsoft Fabric as well as other supported SQL platforms.
This also matches the shown code pattern:
* declare a vector variable such as @query_vector VECTOR(1536),
* create a vector index on dbo.Products(embedding),
* query with VECTOR_SEARCH(... SIMILAR_TO = @query_vector, METRIC = ' cosine ' , TOP_N =
10).
NEW QUESTION # 54
You have a GitHub Actions workflow that builds and deploys an Azure SQL database. The schema is stored in a GitHub repository as an SDK-style SQL database project.
Following a code review, you discover that you need to generate a report that shows whether the production schema has diverged from the model in source control.
Which action should you add to the pipeline?
Answer: A
Explanation:
Microsoft documents that DriftReport creates an XML report showing changes that have been made to the registered database since it was last registered . That is the action intended to detect whether the production schema has diverged from the expected model baseline in your deployment workflow.
This is different from DeployReport , which shows the changes that would be made by a publish action . In other words:
* DriftReport answers: Has the deployed database drifted from the registered state/model?
* DeployReport answers: What changes would be applied if I published now?
The other options are not the right fit:
* Extract creates a DACPAC from an existing database, not a drift analysis report.
* Script generates a deployment script, not a schema-drift report.
So to generate a report that shows whether production has diverged from the model in source control , add:
SqlPackage.exe /Action:DriftReport
NEW QUESTION # 55
Your development team uses Microsoft Visual Studio Code with the MSSQL extension and the GitHub Copilot Chat extension.
The team connects to an Azure SQL database by using individual database logins and uses the
@mssql chat participant to generate and run Transact-SQL queries from prompts.
What is used to ensure that GitHub Copilot Chat-generated queries run in the context of the developer?
Answer: A
Explanation:
To ensure that GitHub Copilot Chat-generated queries run in the context of a specific developer when using the @mssql chat participant, SQL Permissions must be used.
Why SQL Permissions are the Key
When the @mssql extension executes a query generated by Copilot, it uses the active connection currently established in VS Code. Because your team uses individual database logins, the execution context is governed by the following:
Authentication: The developer logs in with their specific credentials.
Authorization: The SQL Server engine checks the SQL Permissions (GRANT/DENY/REVOKE) assigned to that specific database user.
Execution: Any T-SQL command sent by the Copilot chat participant is limited by what that specific login is allowed to do (e.g., SELECT, UPDATE, or DROP).
Reference:
https://learn.microsoft.com/en-us/sql/tools/visual-studio-code-extensions/github-copilot/limitations- and-known-issues
NEW QUESTION # 56
Which data type is used to store vector embeddings in SQL?
Answer: C
Explanation:
Embeddings are stored as arrays or binary/vector types depending on SQL engine support.
NEW QUESTION # 57
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