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NEW QUESTION # 28
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders, dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.
You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.orders.
Solution; Identify the partition scheme (or the oldest month, and then run the following Transact-SQL statement.
ALTER TABLE dbo.Orders
DROP PARTITION SCHEME (partition_scheme_name);
Does this meet the goal?
Answer: A
Explanation:
This also does not meet the goal. DROP PARTITION SCHEME removes the partition scheme object from the database; it is not the command used to remove just the rows for the oldest month from a partitioned table.
Microsoft's DROP PARTITION SCHEME documentation is explicit that the statement removes the partition scheme itself.
For removing only the oldest month's rows with minimal impact, Microsoft points to partition-level maintenance operations such as truncating a single partition on a partitioned table. That targets only the needed data subset and is more efficient for retention workloads.
NEW QUESTION # 29
You have a Microsoft SQL Server 2025 database that contains a table named products. products contains two columns named description and embedding. embedding is generated from description.
You have an application named App1. App1 has a feature that uses the following query.
VECTOR_DISTANCE('cosine', @query_vector, embedding)
Users report that the feature is slow during peak usage times.
You discover that during peak usage times, the semantic search latency increases and CPU utilization spikes.
You need to reduce latency and CPU utilization related to the App1 feature.
What should you do?
Answer: C
Explanation:
To optimize your Microsoft SQL Server 2025 environment, you should transition from a brute- force distance calculation to an Approximate Nearest Neighbor (ANN) search using a DiskANN- based vector index.
The Solution: Vector Indexing
The VECTOR_DISTANCE function performs a full table scan, calculating the distance for every single row. During peak times, this consumes massive CPU cycles. A vector index allows SQL Server to navigate a pre-built graph to find matches, reducing complexity from O(n) to O(log n).
Implementation Steps
1. Create the Vector Index
You must specify the distance metric during index creation. For cosine similarity, use COSINE.
CREATE VECTOR INDEX idx_product_embedding
ON products (embedding)
WITH (METRIC = 'COSINE');
2. Update the Search Query
Replace the WHERE or ORDER BY clause that uses VECTOR_DISTANCE with the VECTOR_SEARCH function.
SELECT TOP (10) description, embedding
FROM products
WHERE VECTOR_SEARCH(
embedding,
@query_vector,
'COSINE',
10 -- Number of neighbors to return
);
Why This Solves Your Problem
CPU Relief: Avoids calculating distances for millions of rows.
Lower Latency: DiskANN is designed for sub-second retrieval even on large datasets.
Peak Performance: By offloading the math to a specialized index structure, the engine remains responsive for other transactional queries.
Reference:
https://learn.microsoft.com/en-us/azure/postgresql/extensions/how-to-use-pgdiskann
NEW QUESTION # 30
Hotspot Question
You have an Azure subscription. The subscription contains an Azure SQL database named SalesDB and an Azure App Service app named sales-api. sales-api uses virtual network integration to a subnet named vnet-prod/subnet-app and reads from SalesDB.
You need to configure authentication and network access to meet the following requirements:
- Ensure that sales-api connects to SalesDB by using passwordless
authentication.
- Ensure that all the database traffic remains within the subscription.
The solution must minimize administrative effort.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 31
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders.
dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.
You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.Orders.
Solution: Run the following Transact-SQL statement.
DELETE FROM dbo.Orders
WHERE OrderDate < DATEADD(month, -36, SYSUTCDATETIME());
Does this meet the goal?
Answer: A
Explanation:
Correct:
* Identify the partition number for the oldest month, and then run the following Transact-SQL statement.
TRUNCATE TABLE dbo.Orders
WITH (PARTITIONS (partition number));
The best Transact-SQL statement to remove all rows for the oldest month while minimizing the impact on other queries is TRUNCATE TABLE with a WITH (PARTITIONS (...)) clause.
Why TRUNCATE TABLE ... WITH (PARTITIONS (...)) is Best
Efficiency: TRUNCATE TABLE is a Data Definition Language (DDL) operation that removes data by deallocating the data pages, which is a metadata operation and is very fast, regardless of the amount of data in the partition.
Minimal Logging: It uses less transaction log space compared to a DELETE statement, which logs each row deletion individually.
Low Impact on Concurrency: It performs a quick, partition-specific operation. A row-by-row DELETE would be a long-running transaction and could cause locking and blocking issues for other queries accessing the table.
Data Integrity: Because the table has a clustered index and is partitioned by the same column (aligned indexes), the TRUNCATE PARTITION operation is a fast, partition-level maintenance operation that targets only that specific data subset.
Incorrect:
* : Identify the partition scheme for the oldest month, and then run the following Transact-SQL statement.
ALTER TABLE dbo.Orders
DROP PARTITION SCHEME (partition_scheme_name);
The DROP PARTITION SCHEME statement removes the partition scheme object from the database but does not remove the data itself or free up the space, and it requires all tables to be moved off the scheme first, which is a complex operation. This does not meet the goal of removing the data efficiently.
* Run the following Transact-SQL statement.
DELETE FROM dbo.Orders
WHERE OrderDate < DATEADD(month, -36, SYSUTCDATETIME());
A standard DELETE statement, even with a WHERE clause that uses the partition column, can be a time-consuming, logged operation that causes locking and blocking on the main table, negatively impacting performance.
Reference:
https://stackoverflow.com/questions/63632963/truncate-partition-vs-drop-partition-performace- wise-which-one-is-efficient-an
NEW QUESTION # 32
You have a GitHub Codespaces environment that has GitHub Copilot Chat installed and is connected to a SQL database in Microsoft Fabric named DB1 DB1 contains tables named Sales.Orders and Sales.Customers.
You use GitHub Copilot Chat in the context of DB1 .
A company policy prohibits sharing customer Personally Identifiable Information (Pll), secrets, and query result sets with any Al service.
You need to use GitHub Copilot Chat to write and review Transact-SQL code for a new stored procedure that will join Sales.Orders to sales .Customers and return customer names and email addresses. The solution must NOT share the actual data in the tables with GitHub Copilot Chat.
What should you do?
Answer: A
Explanation:
The correct answer is D because the policy explicitly prohibits sharing customer PII, secrets, and query result sets with any AI service. The safe way to use GitHub Copilot Chat here is to provide only schema- level information such as table names, column names, relationships, and the required procedure behavior, without sharing actual table contents or result sets. That lets Copilot help generate and review the Transact- SQL while avoiding disclosure of customer data. This is consistent with Microsoft and GitHub guidance that content provided in prompts is what the AI can use, so avoiding real data in the prompt is the appropriate control.
The other options violate the requirement:
* A pastes real rows containing email addresses, which is direct PII disclosure.
* B shares actual query result sets, which the policy forbids.
* C provides the connection string so Copilot can validate against the database, which is inappropriate because it exposes connection details and could enable access beyond schema-only assistance.
So the correct approach is to ask Copilot to generate the stored procedure using only the schema and requirements, not real customer data.
NEW QUESTION # 33
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