DP-300 Practice Question: Monitor, configure, and optimize database resources
Which TWO actions can help you identify and resolve performance bottlenecks related to I/O in an Azure SQL Database?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Query sys.dm_exec_requests and filter on wait_type like PAGEIOLATCH.
Options A and B are correct. A: Querying sys.dm_exec_requests with a filter on PAGEIOLATCH wait type identifies queries waiting for I/O, pinpointing I/O bottlenecks. B: Data compression reduces the size of data pages, decreasing I/O operations for large tables. C: Increasing the service tier adds compute resources (CPU/memory) but does not directly address I/O bottlenecks; it may only mask the issue. D: sys.dm_db_resource_stats provides average I/O per minute at the database level, which is useful for monitoring but not for per-query I/O bottleneck identification. E: sys.dm_os_performance_counters for CPU usage tracks CPU performance, not I/O.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Query sys.dm_exec_requests and filter on wait_type like PAGEIOLATCH.
Why this is correct
PAGEIOLATCH waits indicate I/O bottlenecks.
- ✓
Enable data compression on large tables to reduce I/O.
Why this is correct
Compression reduces page reads, improving I/O.
- ✗
Increase the database service tier to add more compute resources.
Why it's wrong here
Scaling compute may not resolve I/O bottlenecks; storage is separate.
- ✗
Use sys.dm_db_resource_stats to see average I/O per minute.
Why it's wrong here
Shows resource consumption but not per query I/O.
- ✗
Monitor sys.dm_os_performance_counters for CPU usage.
Why it's wrong here
CPU counters don't directly show I/O.
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Related to this question
Learn chapter
Overview of Azure Data Platform Options
Key term
Azure SQL Performance Tuning
Azure SQL Performance Tuning is the process of optimizing the speed and efficiency of queries and database operations in Microsoft Azure SQL Database or SQL Managed Instance to reduce latency and improve throughput.
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