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NEW QUESTION # 12
You have a GitHub Enterprise subscription.
Your team is developing an Azure SQL dataset solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
A mix of GitHub Copilot instructions is configured at different levels, including organization-wide, repository-wide, agent-specific, and personal.
Which instructions will take precedence over the others?
Answer: C
Explanation:
In a GitHub Enterprise environment, personal instructions (user-level) generally have the highest precedence for individual interactions in VS Code.
Order of Precedence
When GitHub Copilot processes instructions, it follows a "specific-to-general" hierarchy. The most specific context (the individual user) typically overrides broader organizational settings.
1. Personal Instructions
Set via .github/copilot-instructions.md in your local home directory or VS Code settings.
Overrides all other layers for your specific session.
2. Agent-Specific / Extension Instructions
Specific instructions provided to a custom agent (like @workspace or a custom Chat participant).
3. Repository-wide Instructions
Stored in the .github/copilot-instructions.md file within the specific repository.
4. Organization-wide Policies
Set by Enterprise/Org admins. These usually act as "guardrails" (e.g., blocking suggestions) rather than stylistic instructions.
Reference:
https://aiddbot.com/vscode-and-github-copilot
NEW QUESTION # 13
You have a SQL database in Microsoft Fabric that contains a nvarchar (max) column named MessageText. An ID is always contained within the first paragraph of MessageText.
You need to write a Transact-SQL query that uses REGEXP_SUBSTR to extract the ID from MessageText.
What should you include in the query?
Answer: B
Explanation:
To extract an ID (e.g., alphanumeric) from the first paragraph of an nvarchar(max) column in Microsoft Fabric using STRING_ESCAPE, use STRING_SPLIT or CHARINDEX to isolate the first paragraph, apply STRING_ESCAPE, and a regex pattern.
Note: T-SQL does not natively support a REGEXP_SUBSTR function like Oracle/Snowflake. The solution below uses STRING_ESCAPE followed by pattern matching via PATINDEX and SUBSTRING to extract a typical alphanumeric ID.
Reference:
https://learn.microsoft.com/en-us/sql/t-sql/queries/contains-transact-sql
NEW QUESTION # 14
Hotspot Question
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:
NEW QUESTION # 15
You have an Azure SQL database That contains a table named dbo.Products, dbo.Products contains three columns named Embedding Category, and Price. The Embedding column is defined as VECTOR(1536).
You use Ai_GENERME_EMBEDOINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3-smalL Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?
Answer: A
NEW QUESTION # 16
You need to design a generative AI solution that uses a Microsoft SQL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
- Are grounded in the latest transactional and reference data stored in DB1
- 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:
Implementing a Retrieval-Augmented Generation (RAG) pattern with Microsoft SQL Server 2025 allows you to ground generative AI responses in real-time company data without the need for model fine-tuning. This version of SQL Server functions as a native vector database, enabling seamless integration between relational data and AI-powered insights.
Incorrect:
[Not B]
Training a custom language model is not a use case for the Retrieval Augmented Generation (RAG) pattern.
In fact, the primary purpose of RAG is to provide a language model with up-to-date information without the need for training, retraining, or fine-tuning.
Reference:
https://devblogs.microsoft.com/azure-sql/sql-server-2025-embraces-vectors-setting-the- foundation-for-empowering-your-data-with-ai
NEW QUESTION # 17
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