Correct Answer: B  🗳️

Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).

Correct Answer:

Explanation:
Verified Answer : =
* CPU aggregation expression # rs.avg_cpu_time
* Runtime interval source # sys.query_store_runtime_stats_interval
* Last two hours filter # DATEADD(HOUR, -2, GETUTCDATE())
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The first correct selection is rs.avg_cpu_time . Query Store stores CPU statistics per aggregation interval, and Microsoft documents avg_cpu_time as the average CPU time per execution, in microseconds . To calculate total CPU consumption across all executions, multiply count_executions by avg_cpu_time , then divide by
1000.0 to convert microseconds to milliseconds:
SUM(count_executions * rs.avg_cpu_time / 1000.0)
This is the exact pattern Microsoft uses in its documented query for identifying the top 15 CPU-consuming queries by query hash .
The second selection is sys.query_store_runtime_stats_interval because sys.query_store_runtime_stats.
runtime_stats_interval_id is a foreign key to this view. The interval view provides start_time and end_time , allowing Query Store data to be limited to the required time window.
The third selection is DATEADD(HOUR, -2, GETUTCDATE()) . Microsoft's own Azure SQL performance- monitoring example for the last two hours uses exactly:
rsi.start_time > = DATEADD(HOUR, -2, GETUTCDATE())
and then ranks by total CPU descending to return the top 15 query hashes.
Final drag-and-drop selections:
* First target: rs.avg_cpu_time
* Second target: sys.query_store_runtime_stats_interval
* Third target: DATEADD(HOUR, -2, GETUTCDATE())

Correct Answer:

Explanation:

These are the correct selections for a hybrid query that uses semantic ranking in Azure AI Search.
Use k = 50 because Microsoft explicitly recommends that when you combine semantic ranking with vector queries , you should set k to 50 so the semantic ranker has enough candidates to rerank. If you use a smaller value such as 10, semantic ranking can receive too few inputs, which is exactly why some queries return fewer results than expected.
Use queryType = " semantic " because captions and answers are only available on semantic queries.
Microsoft documents that captions is valid only when the query type is semantic, and semantic answers are returned only for semantic queries.
Use captions = " extractive " because semantic captions are extractive passages pulled from the top-ranked documents. Microsoft's REST documentation states that the valid captions option here is extractive and that it defaults to none if not specified.
Use answers = " extractive " because semantic answers in Azure AI Search are extractive, not generated.
Microsoft documents that semantic answers are verbatim passages recognized as answers and the REST API lists extractive as the answer-return option.

Correct Answer: D  🗳️

Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).

Correct Answer:

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 '