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NEW QUESTION # 50
Which service enables natural language querying over SQL data?
Answer: B
Explanation:
Azure OpenAI Service allows users to query structured SQL data using natural language via LLMs.
NEW QUESTION # 51
You have an Azure SQL database that contains a table named Rooms. Roomswas created by using the following Transact-SQL statement.
You discover that some records in the Rooms table contain NULL values for the Owner field.
You need to ensure that all future records have a value for the Owner field.
What should you add?
Answer: D
Explanation:
A CHECK constraint is one way to do it.
If you use a CHECK constraint (e.g., CHECK (ColumnName IS NOT NULL)), the database will indeed reject new NULL entries. However, the column's metadata will still technically allow NULLs, which can sometimes affect how external tools or APIs interact with your schema.
Reference:
https://www.postgresql.org/docs/7.0/sql-createtable.htm
NEW QUESTION # 52
Hotspot Question
You have an Azure SQL database that contains a table named knowledge_base.
knowledge_base stores human resources (HR) policy documents and contains columns named title, content, category, and embedding.
You have an application named App1. App1 queries two relational tables named employee_profiles and benefits_enrollment that contain HR data. App1 hosts a chatbot that calls a large language model (LLM) directly.
Users report that the chatbot answers general HR questions correctly but provides outdated or incorrect answers when policies change. The chatbot also fails to answer questions that reference internal policy documents by title or category.
You need to recommend a Retrieval Augmented Generation (RAG) solution to resolve the chatbot issues.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 53
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 # 54
Your team is developing an Azure SQL database solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
You need to ensure that GitHub Copilot Chat uses the team's coding standards when generating Transact-SQL code in Visual Studio Code.
What should you use?
Answer: D
Explanation:
o ensure GitHub Copilot Chat adheres to your team's specific Transact-SQL (T-SQL) standards while deploying an Azure SQL database from VS Code, you should focus on Custom Instructions and Workspace Context.
Required Setup
Create a .github/copilot-instructions.md File
This is the most effective way to enforce standards.
Create this file in the root of your repository.
Copilot automatically reads this file to understand project-specific rules.
Include sections for:
Naming conventions (e.g., PascalCase for tables, proc_ prefix for stored procedures).
Formatting rules (e.g., keywords in UPPERCASE, use of 4 spaces).
Security practices (e.g., always use schema prefixes, avoid SELECT *).
Reference:
https://nikolay-dev.medium.com/master-web-development-with-github-spark-ai-b0b874525418
NEW QUESTION # 55
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