Quick Answer
The answer is page_cache_hit_ratio, avg_page_life_expectancy, and avg_log_write_usage_percent. Page_cache_hit_ratio measures the percentage of data requests served from the buffer pool without physical I/O; a drop below 90% signals that memory pressure is forcing frequent disk reads. Avg_page_life_expectancy tracks how long a data page stays in the buffer pool in seconds—values under 300 seconds indicate pages are being evicted too quickly due to insufficient memory, directly correlating with increased I/O. Avg_log_write_usage_percent reveals when log writes are throttled because the buffer pool is starved for memory, a less obvious but critical symptom. On the DP-300 exam, Microsoft tests your ability to distinguish memory pressure from CPU or storage bottlenecks; a common trap is confusing avg_page_life_expectancy with page_cache_hit_ratio, but remember that life expectancy is about page *duration* while hit ratio is about page *success*. Memory tip: "PLE below 300, your pages are not ready—check the cache hit ratio to see if they're steady."
This DP-300 practice question tests your understanding of monitor, configure, and optimize database resources. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE metrics should you monitor to detect a memory pressure issue in Azure SQL Database?
avg_io_stall_ms
Why wrong: I/O stall is an I/O metric.
avg_page_life_expectancy
Lower page life expectancy indicates memory pressure.
log_bytes_used
Why wrong: Log bytes used is a log throughput metric.
avg_pending_disk_io
High pending I/O can be caused by memory pressure.
page_cache_hit_ratio
Lower hit ratio indicates memory pressure.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
avg_page_life_expectancy
B (avg_page_life_expectancy) is correct because it measures how long (in seconds) a data page stays in the buffer pool before being evicted. A low value (typically below 300 seconds) indicates that pages are being flushed quickly due to memory pressure, forcing more physical I/O. This is a direct indicator of insufficient memory for the buffer cache.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
avg_io_stall_ms
Why it's wrong here
I/O stall is an I/O metric.
avg_page_life_expectancy
Why this is correct
Lower page life expectancy indicates memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
log_bytes_used
Why it's wrong here
Log bytes used is a log throughput metric.
avg_pending_disk_io
Why this is correct
High pending I/O can be caused by memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
page_cache_hit_ratio
Why this is correct
Lower hit ratio indicates memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse I/O-related metrics (like avg_io_stall_ms or avg_pending_disk_io) with memory pressure, but those metrics indicate storage performance issues, not insufficient memory for the buffer pool.
Detailed technical explanation
avg_page_life_expectancy is calculated as the number of pages in the buffer pool divided by the page flush rate per second. In Azure SQL Database, a value below 300 seconds often triggers a recommendation to increase the service tier or optimize memory usage. Real-world scenarios include large index scans or hash joins that flood the buffer pool with cold pages, rapidly decreasing page life expectancy even if total memory is adequate.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Monitor, configure, and optimize database resources — This question tests Monitor, configure, and optimize database resources — Read the scenario before looking for a memorised answer..
The correct answer is: avg_page_life_expectancy — B (avg_page_life_expectancy) is correct because it measures how long (in seconds) a data page stays in the buffer pool before being evicted. A low value (typically below 300 seconds) indicates that pages are being flushed quickly due to memory pressure, forcing more physical I/O. This is a direct indicator of insufficient memory for the buffer cache.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE metrics should you monitor to proactively detect potential performance issues in an Azure SQL Database?
hardWhy A: Options A, B, and D are correct. Wait statistics (A) show where queries are waiting. Query Store (B) tracks plan regression and performance. Log IO (D) indicates transaction log throughput bottlenecks. Option C is wrong because it shows storage size, not performance. Option E is wrong because log backup frequency affects recovery, not performance.
Last reviewed: Jun 11, 2026
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