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NEW QUESTION # 18
Hotspot Question
You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB 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:
NEW QUESTION # 19
Which component handles security in Azure SQL?
Answer: B
Explanation:
Azure Key Vault securely stores secrets and keys.
NEW QUESTION # 20
You have an Azure SQL database named SalesDB that contains a table named dbo. Articles, dbo.Articles contains two million articles with embeddmgs. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARQi
Users report that semantic search results do NOT reflect the updates until the following day.
Vou need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB Which embedding maintenance method should you implement?
Answer: B
Explanation:
The correct answer is B because the problem is not the vector search operator itself. The problem is that embeddings are becoming stale when article content changes . Microsoft documents that change data capture (CDC) tracks insert, update, and delete operations on source tables, which makes it the right mechanism to identify only the rows that changed.
This also best satisfies the requirement to minimize CPU usage on SalesDB . With CDC, the database only records the row changes, and the embedding regeneration work can be moved to an external process such as an Azure Functions app. That avoids running embedding generation inline inside the database for every update and avoids repeatedly recalculating embeddings for unchanged rows. In contrast, an hourly full-table regeneration would be extremely wasteful on a table with two million frequently updated articles, and a trigger that calls embedding generation per row would push expensive AI work into the transactional path of the database.
Option A is incorrect because changing from VECTOR_SEARCH to VECTOR_DISTANCE does not regenerate embeddings; it only changes the retrieval method. Microsoft states that VECTOR_SEARCH is the ANN search function, while VECTOR_DISTANCE performs exact distance calculation, so neither option addresses stale embedding data.
So the right design is:
* use CDC to detect only changed articles,
* process those changes outside the database,
* regenerate embeddings only for changed rows,
* write back the refreshed embeddings for current semantic search results.
NEW QUESTION # 21
What is Retrieval-Augmented Generation (RAG)?
Answer: B
Explanation:
RAG enhances LLM responses by retrieving relevant SQL data before generating answers.
NEW QUESTION # 22
You have an Azure SQL database that contains a table named Customer. Customer contains the following columns:
- NationalIDNumber is unique per customer.
- InvestigationNotes contains free-form text.
You have a stored procedure that performs point lookups by NationalIDNumber and returns InvestigationNotes in the query results without filtering on InvestigationNotes.
You need to encrypt both columns by using Always Encrypted and the highest security possible.
Which type of Always Encrypted encryption should you use for each column?
Answer: C
Explanation:
To achieve the highest security for an Azure SQL Database using Always Encrypted, you must use Randomized Encryption for the InvestigationNotes and Deterministic Encryption for the NationalIDNumber.
While "Randomized" is more secure, it does not support point lookups (equality searches).
Therefore, a hybrid approach is required to maintain functionality while maximizing protection.
Recommended Encryption Settings
* NationalIDNumber
Deterministic is required to allow the Stored Procedure to perform point lookups (WHERE NationalIDNumber = @id).
* InvestigationNotes
Use randomized as it provides the highest security. It prevents pattern matching and "leakage" for sensitive text.
Reference:
https://medium.com/@ih_hira/implementing-always-encrypted-with-azure-key-vault-and-entity- framework-core-in-azure-sql-part-1-3a0e5fd7ed9b
NEW QUESTION # 23
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