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>> DP-800 Exam Questions And Answers <<
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NEW QUESTION # 94
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 # 95
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 # 96
You have an Azure SQL database named ProductsDB
You deploy Data API builder (OAB) to Azure Container Apps.
You discover that the container app cannot connect to ProductsDB
Your development team reports that the container app is unreachable from the internet tor integration tests.
You need to update Azure SQL Database and Container Apps to meet the following requirements:
* Ensure that the Azure SQL logical server allows connections from Azure services.
* Ensure that the Container Apps environment accepts inbound requests from the public internet.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 97
You have an Azure SQL database that contains a table named stores, stores contains a column named description and a vector column named embedding.
You need to implement a hybrid search query that meets the following requirements:
* Uses full-text search on description for the keyword portion
* Returns the top 20 results based on a combined score that uses a weighted formula of 60% vector distance and 40% full-text rank How should you configure the query components? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For the vector portion, the correct choice is VECTOR_DISTANCE and order by distance ascending . The requirement is to build a combined weighted formula using the actual vector distance. Microsoft documents that VECTOR_DISTANCE returns the exact distance between two vectors. Since lower distance means greater similarity, ascending distance is the right direction for ranking. VECTOR_SEARCH is for ANN retrieval, but this hotspot specifically asks for a weighted formula based on distance , so VECTOR_DISTANCE is the appropriate operator.
For the keyword portion, the correct choice is CONTAINSTABLE on description and return ranked matches . Microsoft documents that CONTAINSTABLE returns a RANK column from 0 through 1000 , which is exactly what is needed for weighted scoring in a hybrid formula.
For the final ranking expression, the best choice is order by (distance * 0.6) + ((1.0 - RANK/1000.0) * 0.4) .
This works because vector distance is a lower-is-better metric, while full-text RANK is a higher-is-better metric. Dividing RANK by 1000 normalizes it to the documented range, and subtracting from 1.0 converts it into a lower-is-better term so both components can be combined consistently in one ascending score. This final step is a sound inference based on Microsoft's documented distance semantics and full-text rank range.
NEW QUESTION # 98
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 # 99
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