- A
Swap the order to 'timestamp#advertiser_id#campaign_id'
Putting timestamp first distributes writes as timestamps are monotonically increasing.
- B
Use a single table per advertiser
Why wrong: Multiple tables don't improve distribution within a table and add complexity.
- C
Increase the number of Bigtable nodes to 100
Why wrong: More nodes won't fix hotspotting caused by poor row key design.
- D
Use a composite key with campaign_id first
Why wrong: If campaign_id is not well-distributed, it may still cause hotspots.
- E
Add a salt (hash of advertiser_id) as the first component
Salting randomizes the row key prefix and spreads writes across tablets.
Question 760 of 999
PCD Practice Question: Design Scalable and Highly Available Cloud Database Solutions
This PCD practice question tests your understanding of design scalable and highly available cloud database solutions. 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.
A data engineer is designing a Bigtable schema for a global ad-tech platform. The workload includes time-series clickstream data with row keys like 'advertiser_id#campaign_id#timestamp'. They are experiencing uneven load distribution. Which TWO row key design changes would best distribute writes across nodes? (Choose two)
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
✓
Swap the order to 'timestamp#advertiser_id#campaign_id'
Option A is correct because placing the timestamp first in the row key ensures that writes are spread across the Bigtable tablet server range. In Bigtable, rows are sorted lexicographically by row key; leading with a monotonically increasing value (like timestamp) causes all new writes to hit a single tablet, creating a hotspot. By swapping to 'timestamp#advertiser_id#campaign_id', writes are still sequential but the timestamp prefix alone does not guarantee perfect distribution—however, combined with salting (Option E), it avoids the original hotspot caused by the advertiser_id prefix.
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.
- ✓
Swap the order to 'timestamp#advertiser_id#campaign_id'
Why this is correct
Putting timestamp first distributes writes as timestamps are monotonically increasing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a single table per advertiser
Why it's wrong here
Multiple tables don't improve distribution within a table and add complexity.
- ✗
Increase the number of Bigtable nodes to 100
Why it's wrong here
More nodes won't fix hotspotting caused by poor row key design.
- ✗
Use a composite key with campaign_id first
Why it's wrong here
If campaign_id is not well-distributed, it may still cause hotspots.
- ✓
Add a salt (hash of advertiser_id) as the first component
Why this is correct
Salting randomizes the row key prefix and spreads writes across tablets.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that simply reordering row key components (like putting timestamp first) is sufficient to fix hotspots, when in reality a monotonically increasing prefix still causes sequential writes to a single tablet unless combined with salting or a non-sequential prefix.
Detailed technical explanation
How to think about this question
Bigtable splits rows into tablets by row key range; a monotonically increasing prefix (like timestamp) causes all new writes to land on the last tablet, creating a hotspot. Salting (Option E) prepends a hash of a high-cardinality field (e.g., advertiser_id) to distribute writes across the key space uniformly. In practice, a common pattern is to use a hash prefix (e.g., SHA256 of advertiser_id mod N) followed by timestamp to balance write throughput while still enabling range scans for time-series queries.
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
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FAQ
Questions learners often ask
What does this PCD question test?
Design Scalable and Highly Available Cloud Database Solutions — This question tests Design Scalable and Highly Available Cloud Database Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Swap the order to 'timestamp#advertiser_id#campaign_id' — Option A is correct because placing the timestamp first in the row key ensures that writes are spread across the Bigtable tablet server range. In Bigtable, rows are sorted lexicographically by row key; leading with a monotonically increasing value (like timestamp) causes all new writes to hit a single tablet, creating a hotspot. By swapping to 'timestamp#advertiser_id#campaign_id', writes are still sequential but the timestamp prefix alone does not guarantee perfect distribution—however, combined with salting (Option E), it avoids the original hotspot caused by the advertiser_id prefix.
What should I do if I get this PCD 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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Last reviewed: Jul 4, 2026
This PCD 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 PCD exam.
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