- A
Enable the pgzstd extension to compress data in memory.
Why wrong: Data compression in memory is not a typical solution for out-of-memory errors and may add CPU overhead.
- B
Set up connection pooling using PgBouncer to limit the number of concurrent connections.
Why wrong: Pooling reduces memory per connection but the main memory usage comes from query operations, not connections.
- C
Scale up the instance to a higher memory tier, such as 8 vCPUs and 30 GB of memory.
More memory will accommodate the reporting queries' work_mem and cache needs, reducing out-of-memory errors.
- D
Decrease the shared_buffers configuration parameter to free memory for other uses.
Why wrong: Shared_buffers is important for caching; reducing it will likely degrade query performance and may not free enough memory.
Quick Answer
The answer is to scale up the instance to a higher memory tier, such as 8 vCPUs and 30 GB of memory, because resolving memory pressure in Cloud SQL PostgreSQL requires directly addressing the root cause of the out of memory errors. When complex read-only reporting queries consume over 90% of memory, increasing the instance’s RAM provides the necessary headroom for query work_mem and shared buffers without sacrificing performance. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding that scaling up is the most effective response to persistent memory exhaustion in a read-heavy workload, especially after pgtune recommendations have already been applied. A common trap is to reduce shared_buffers, which can degrade query speed, or to rely solely on connection pooling, which doesn’t solve the overall memory shortage. Remember the memory tip: when memory is the bottleneck, scale up the bucket, not just the spigot.
PCDE Monitor and optimize database performance Practice Question
This PCDE practice question tests your understanding of monitor and optimize database performance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
You administer a Cloud SQL for PostgreSQL instance running with 4 vCPUs and 15 GB of memory. The application frequently runs complex read-only reporting queries during business hours. Recently, the database started throwing 'out of memory' errors, and the instance's memory usage is consistently above 90%. You have enabled the pgtune recommendations and applied them. The workload is read-heavy and does not require a high write throughput. Which change would most effectively reduce memory pressure while maintaining query performance?
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
Scale up the instance to a higher memory tier, such as 8 vCPUs and 30 GB of memory.
Option B is correct: Increasing the instance memory directly addresses the memory shortage. Option A (reducing shared_buffers) may free memory but hurt performance. Option C (using connection pooling) reduces memory per connection but not overall memory needs. Option D (enabling compression) might help with I/O but not memory very much; also, pgzstd is a tricky flag.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the pgzstd extension to compress data in memory.
Why it's wrong here
Data compression in memory is not a typical solution for out-of-memory errors and may add CPU overhead.
- ✗
Set up connection pooling using PgBouncer to limit the number of concurrent connections.
Why it's wrong here
Pooling reduces memory per connection but the main memory usage comes from query operations, not connections.
- ✓
Scale up the instance to a higher memory tier, such as 8 vCPUs and 30 GB of memory.
Why this is correct
More memory will accommodate the reporting queries' work_mem and cache needs, reducing out-of-memory errors.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the shared_buffers configuration parameter to free memory for other uses.
Why it's wrong here
Shared_buffers is important for caching; reducing it will likely degrade query performance and may not free enough memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which PCDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PCDE question test?
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Scale up the instance to a higher memory tier, such as 8 vCPUs and 30 GB of memory. — Option B is correct: Increasing the instance memory directly addresses the memory shortage. Option A (reducing shared_buffers) may free memory but hurt performance. Option C (using connection pooling) reduces memory per connection but not overall memory needs. Option D (enabling compression) might help with I/O but not memory very much; also, pgzstd is a tricky flag.
What should I do if I get this PCDE question wrong?
Identify which PCDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This PCDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDE exam.
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