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.
An e-commerce platform uses Cloud Bigtable for real-time user sessions. Write latency is high. On investigation, they find that rows are being written with monotonically increasing row keys (e.g., user_id + timestamp). What is the likely cause and solution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Hotspotting on a single node; use salting or field promotion
Monotonically increasing row keys (e.g., user_id + timestamp) cause all writes to target a single tablet server, creating a hotspot. Cloud Bigtable distributes writes across nodes by row key range; sequential keys concentrate load on one node, degrading write latency. Salting (prepending a hash or random prefix) or field promotion (using a high-cardinality field as the first part of the key) spreads writes evenly across the cluster.
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.
✗
Too many column families; merge them
Why it's wrong here
Too many column families can cause other overhead but not hotspotting from key pattern.
✗
Inefficient reads; use reverse scan
Why it's wrong here
Reverse scan helps read order but does not fix write hotspotting.
✓
Hotspotting on a single node; use salting or field promotion
Why this is correct
Salting or field promotion spreads writes across tablets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Tablet splits are misconfigured; pre-split the table
Why it's wrong here
Pre-splitting helps initial load but does not prevent hotspotting from sequential keys.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that pre-splitting alone solves hotspotting, but the trap here is that monotonically increasing keys will still cause writes to concentrate on the last tablet regardless of pre-splitting, requiring key design changes like salting.
Detailed technical explanation
How to think about this question
Cloud Bigtable uses a shared-nothing architecture where each tablet is served by a single node; writes to sequential keys hit the same tablet, causing that node's CPU and I/O to saturate while others remain idle. Salting can be implemented by hashing the user_id and taking the first few bytes as a prefix, or by using a field like region_id that naturally distributes load. In production, a common pattern is to use a reverse timestamp (e.g., MAX_TIMESTAMP - timestamp) to avoid sequential writes while preserving time-ordering for scans.
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.
TExam Day Tips
→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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Hotspotting on a single node; use salting or field promotion — Monotonically increasing row keys (e.g., user_id + timestamp) cause all writes to target a single tablet server, creating a hotspot. Cloud Bigtable distributes writes across nodes by row key range; sequential keys concentrate load on one node, degrading write latency. Salting (prepending a hash or random prefix) or field promotion (using a high-cardinality field as the first part of the key) spreads writes evenly across the cluster.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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