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NEW QUESTION # 53
You have an Azure SQL database that supports a customer-facing API. The API calls a stored procedure named dbo.GetCustomerOrders thousands of times per hour.
After a deployment that updated indexes and statistics, users report that the API endpoint backed by dbo.
Getcustomerorders is slower. In Query Store, the same query now has two persisted execution plans. During the last hour, the newer plan had a significantly higher average duration and CPU time than the older plan.
You need to restore the previous performance quickly, without changing the API code.
Which Transact-SQL command should you run?
Answer: B
Explanation:
The scenario says Query Store already shows two persisted execution plans for the same query, and the older plan performed much better than the newer one during the last hour. Microsoft documents that sp_query_store_force_plan is used to force a particular plan for a particular query in Query Store .
That makes it the fastest way to restore the previously good plan without changing application code , which is exactly what the question requires.
Why the other options are not the best fit:
* sp_query_store_set_hints is for adding or updating Query Store hints to influence compilation behavior, but when you already know the exact older good plan, Microsoft points to plan forcing as the direct remedy.
* DBCC FREEPROCCACHE clears cached plans broadly and is disruptive; it does not guarantee a return to the known good plan.
* ALTER DATABASE is too general and does not directly restore the prior execution plan.
So the right Transact-SQL command is:
EXEC sp_query_store_force_plan
using the relevant @query_id and @plan_id from Query Store for the older, better-performing plan.
Microsoft also notes that when a plan is forced, SQL Server tries to use that plan whenever it encounters the query again.
NEW QUESTION # 54
You have an Azure SQL database that contains a table named Table1. Table1 contains 25,000,000 rows of data and a datetime2 column named DateKey. The data in Table1 spans the years 2020 through 2021.
You need to partition the data in Table1 by year. The solution must minimize how long it takes to rebuild or reindex the table.
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:
Explanation:
Partition range # RANGE RIGHT
Boundary values # ' 2020-01-01 00:00:00 ' , ' 2021-01-01 00:00:00 '
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents: = The correct configuration is to use RANGE RIGHT with boundary values at the start of each year:
CREATE PARTITION FUNCTION PartitionByYear (datetime2)
AS RANGE RIGHT
FOR VALUES (
' 2020-01-01 00:00:00 ' ,
' 2021-01-01 00:00:00 '
);
Microsoft documents that with RANGE RIGHT, each boundary value belongs to the partition on its right. For date-based partitioning, this is the natural pattern because a boundary such as 2021-01-01 becomes the lower boundary of the 2021 partition.
With these boundaries, the resulting ranges are effectively:
Partition 1: dates before 2020-01-01
Partition 2: 2020-01-01 through before 2021-01-01
Partition 3: 2021-01-01 and later
This cleanly separates the 2020 and 2021 data into year-aligned partitions. Microsoft specifically recommends RANGE RIGHT for date-based boundaries because the first day of a period remains in the same partition as the rest of that period.
The other boundary choices are incorrect or less appropriate:
Using year-end timestamps with RANGE LEFT is more cumbersome and can be sensitive to datetime2 precision.
Monthly boundaries would partition by month, not year.
Including 2019 and a 2021-12-31 23:59:59 boundary creates unnecessary partitions and is not the cleanest year-based design.
Therefore:
First dropdown: RANGE RIGHT
Second dropdown: ' 2020-01-01 00:00:00 ' , ' 2021-01-01 00:00:00 '
NEW QUESTION # 55
You have a GitHub Actions workflow that builds and deploys an Azure SQL database. The schema is stored in a GitHub repository as an SDK-style SQL database project.
Following a code review, you discover that you need to generate a report that shows whether the production schema has diverged from the model in source control.
Which action should you add to the pipeline?
Answer: D
Explanation:
To generate a report showing the differences between your source control and the production database, you should use the sql-action (officially Azure/sql-action@v2).
Specifically, you need to set the action parameter to DriftReport.
When configured this way, the action compares your .dacpac file (built from your SQL project) against the target production database. It generates an HTML or XML report detailing any "drift"-- changes made directly to production that aren't in your code.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sqlpackage/sqlpackage-deploy-drift-report
NEW QUESTION # 56
Drag and Drop Question
You have a SQL database in Microsoft Fabric that contains a table named WebSite.Logs.
WebSite.Logs stores application telemetry data. WebSite.Logs contains a nvarchar (max) column named log that stores JSON documents.
You have a daily report that filters by the $.severity JSON property and returns LogId, LogDateTime, and log. The report frequently causes full table scans.
You need to modify WebSite.Logs to support efficient filtering by $.severity and avoid key lookups for the columns returned by the report.
How should you complete the Transact-SQL code to avoid full table scans? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 57
You have an Azure SQL database that stores order data.
A reporting query aggregates monthly revenue per customer runs frequently.
You need to reduce how long it takes to retrieve the calculated values. The solution must NOT alter any underlying table structure.
What should you do?
Answer: D
Explanation:
Creating an indexed view using WITH SCHEMABINDING, including COUNT_BIG(*), and creating a unique clustered index is a valid and effective approach to significantly improve the performance of your frequent reporting query without altering the underlying table structure.
This process materializes the aggregated data and stores it physically in the database, so the query optimizer can read from the precomputed view rather than rescanning the base tables every time the query runs.
Reference:
https://learn.microsoft.com/en-us/sql/relational-databases/views/create-indexed-views
NEW QUESTION # 58
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