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NEW QUESTION # 39
Hotspot Question
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEmbeddings and DescriptionEmbeddings tables have been generated from values in the Description and Notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables.
The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 40
Hotspot Question
You have an Azure SQL database that contains a table named Sales.Customer. Sales.Customer contains columns named CustomerId, FullName, Email, TaxID, and RegionId.
You have a database role named AppSupport that is used by a support application.
You need to implement a security solution for AppSupport that meets the following requirements:
- AppSupport must be prevented from viewing TaxID.
- AppSupport must be able to query Sales.Customer to troubleshoot
issues.
- AppSupport must be able to run a stored procedure named
Sales.usp_GetCustomerByCustomerId.
Which Transact-SQL statements should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 41
You have a database named DB1. The schema is stored in a Git repository as an SDK-style SQL database project.
You have a GitHub Actions workflow that already runs dotnet build and produces a database artifact.
You need to add a deployment step that publishes the dacpac file to an Azure SQL database by using the secrets stored in GitHub repository secrets What should you include in the workflow?
Answer: B
Explanation:
The correct workflow step is Option C because it uses the Azure SQL GitHub Action to publish a .dacpac file and reads the connection string from GitHub repository secrets , which is exactly what the requirement asks for. Microsoft's Azure SQL GitHub Actions guidance shows using azure/sql-action@v2 with a connection string stored in secrets and a DACPAC path for deployment.
The key parts that make C correct are:
* uses: azure/sql-action@v2
* action: publish
* path: bin/Debug/db1.dacpac
* connection-string: ${{ secrets.SQL_CONNECTION_STRING }}
That matches the documented publish pattern for deploying a DACPAC to Azure SQL Database from GitHub Actions. Microsoft and the Azure SQL action documentation both describe Publish as the deployment action for applying a DACPAC to a target database, while Extract is used to create a DACPAC from an existing database, not deploy one.
Why the other options are incorrect:
* A uses an environment variable defined inline with a visible connection string rather than using GitHub repository secrets , which does not meet the requirement.
* B uses action: extract, which would create a DACPAC from a database instead of publishing the existing DACPAC artifact.
* D passes a target connection string to dotnet build, but the question says the workflow already runs dotnet build and produces a database artifact . The missing step is the deployment/publish step, not another build step. Microsoft's SQL project automation guidance separates build the DACPAC from publish the DACPAC .
NEW QUESTION # 42
Your development team uses GitHub Copilot Chat in Microsoft SQL Server Management Studio (SSMS) to generate and run Transact-SQL queries against an Azure SQL database named DB1.
DB1 contains tables that store sensitive customer data.
You need to ensure that any Transact-SQL queries that run from GitHub Copilot Chat in SSMS are restricted by the same permissions as the developer's database login.
What prevents the GitHub Copilot Chat-run queries from accessing data beyond the developer's access?
Answer: A
Explanation:
GitHub Copilot Chat in SSMS acts as an extension of the user, meaning it does not have its own separate service account or elevated privileges.
It operates within the security context of your active connection. If your database login is restricted by Role-Based Access Control (RBAC), Row-Level Security (RLS), or specific DENY permissions on sensitive tables, Copilot cannot bypass those hurdles to fetch or manipulate data you couldn't otherwise access manually.
Reference:
https://learn.microsoft.com/en-us/ssms/github-copilot/chat
NEW QUESTION # 43
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 # 44
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