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 are running a Cloud Bigtable instance for time-series data ingestion. Write throughput has dropped significantly, and you see an increase in 'resource exhausted' errors. The table has one column family and one rowkey format: `#orgId#deviceId#timestamp`. After analyzing cluster metrics, you see that one node is handling most of the traffic. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The rowkey design is causing hotspotting on one tablet node due to a prefix (orgId) being written heavily.
The rowkey design `#orgId#deviceId#timestamp` causes hotspotting because all writes for a given `orgId` are directed to a single tablet node. Cloud Bigtable partitions data by rowkey range, and sequential or heavily skewed prefixes (like `orgId`) concentrate write traffic on one node, leading to 'resource exhausted' errors and throughput degradation.
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.
✗
The column family design is creating too many columns.
Why it's wrong here
Column families do not affect distribution.
✓
The rowkey design is causing hotspotting on one tablet node due to a prefix (orgId) being written heavily.
Why this is correct
If one orgId dominates writes, all writes go to a single tablet.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The cluster does not have enough nodes.
Why it's wrong here
Adding nodes would not redistribute writes until you fix the rowkey.
✗
The timestamp is too granular, causing many rows with the same timestamp.
Why it's wrong here
Different timestamps spread writes across tablets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling nodes (Option C) solves performance issues, but the real problem is rowkey design causing uneven load distribution, which cannot be fixed by adding nodes alone.
Detailed technical explanation
How to think about this question
Cloud Bigtable uses a distributed storage system where each tablet is served by a single node. Rowkeys are sorted lexicographically, so a prefix like `orgId` groups all related rows together, causing all writes for that prefix to land on the same tablet. To avoid hotspotting, rowkeys should be designed to spread writes across tablets—for example, by salting with a hash or reversing the timestamp to use a high-cardinality prefix.
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: The rowkey design is causing hotspotting on one tablet node due to a prefix (orgId) being written heavily. — The rowkey design `#orgId#deviceId#timestamp` causes hotspotting because all writes for a given `orgId` are directed to a single tablet node. Cloud Bigtable partitions data by rowkey range, and sequential or heavily skewed prefixes (like `orgId`) concentrate write traffic on one node, leading to 'resource exhausted' errors and throughput degradation.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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