This PCDE practice question tests your understanding of design and implement database schemas. 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.
Network Topology
Refer to the exhibit. What is the most likely performance issue with this schema?
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.
Network Topology
A
No performance issue; the schema is optimal
Why wrong: The monotonically increasing key will cause hotspotting.
B
Hotspotting on UserId due to frequent queries
Why wrong: UserId is not part of the primary key; hotspotting on primary key is the concern.
C
Hotspotting on TransactionId due to monotonically increasing values
Monotonically increasing keys cause all writes to target a single split.
D
Too many secondary indexes causing write amplification
The answer is hotspotting on TransactionId due to monotonically increasing values. This occurs because Cloud Spanner distributes data across splits based on the primary key range, and a sequentially increasing key like TransactionId forces all new writes to the same split, creating a write hotspot that throttles throughput. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of Cloud Spanner’s architecture and the critical need to avoid write hotspotting in Cloud Spanner due to monotonically increasing primary keys. A common trap is assuming any unique field is safe, but the key insight is that Cloud Spanner’s automatic splitting works best with evenly distributed key prefixes, not sequential ones. Remember the mnemonic: “Hot keys burn splits” — if your primary key grows in one direction, your writes will bottleneck on the last split.
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
✓
Hotspotting on TransactionId due to monotonically increasing values
The schema uses TransactionId as the partition key with monotonically increasing values (e.g., timestamps or auto-incrementing integers). In a distributed database like Cloud Spanner or Bigtable, this causes all writes to land on a single partition, creating a hotspot that throttles throughput and increases latency. The correct answer is C because this hotspotting is the most likely performance issue.
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.
✗
No performance issue; the schema is optimal
Why it's wrong here
The monotonically increasing key will cause hotspotting.
✗
Hotspotting on UserId due to frequent queries
Why it's wrong here
UserId is not part of the primary key; hotspotting on primary key is the concern.
✓
Hotspotting on TransactionId due to monotonically increasing values
Why this is correct
Monotonically increasing keys cause all writes to target a single split.
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.
✗
Too many secondary indexes causing write amplification
Why it's wrong here
No secondary indexes are defined.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is the assumption that any unique identifier works as a partition key. Google Cloud exams test that monotonically increasing values (e.g., timestamps) as partition keys create hotspotting in distributed databases like Spanner or Bigtable, limiting write scalability.
Detailed technical explanation
How to think about this question
In Cassandra, the partition key determines which node stores the data; monotonically increasing keys (e.g., timestamps) cause all new writes to target the last node in the token ring, creating a hot spot. This can be mitigated by using a composite partition key (e.g., UserId + TransactionId) or by bucketing with a hash prefix. In DynamoDB, the same principle applies: a partition key with sequential values leads to throttling on a single partition, and adaptive capacity may help but does not eliminate the design flaw.
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.
Design and implement database schemas — This question tests Design and implement database schemas — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Hotspotting on TransactionId due to monotonically increasing values — The schema uses TransactionId as the partition key with monotonically increasing values (e.g., timestamps or auto-incrementing integers). In a distributed database like Cloud Spanner or Bigtable, this causes all writes to land on a single partition, creating a hotspot that throttles throughput and increases latency. The correct answer is C because this hotspotting is the most likely performance issue.
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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