- A
timestamp_millis#hash(userID)
Why wrong: Timestamp prefix again causes hotspotting.
- B
timestamp_millis#userID
Why wrong: Timestamp prefix causes hotspotting; all current writes go to one tablet server.
- C
userID#timestamp_millis
Why wrong: Plain userID as prefix can still hotspot if a few users generate most traffic.
- D
hash(userID)#userID#timestamp_millis
Hashing the userID distributes writes evenly. Including userID and timestamp enables efficient queries per user over time.
PDE Storing the Data Practice Question
This PDE practice question tests your understanding of storing the data. 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 designing a row key for Cloud Bigtable to store user activity logs. Each log entry has a timestamp (millisecond precision) and a user ID. There will be millions of writes per second from many users. To avoid hotspotting, which row key design is BEST?
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
hash(userID)#userID#timestamp_millis
Option D is best because it uses a hash of the user ID as the row key prefix, which distributes writes across all Bigtable nodes and avoids hotspotting. Appending the user ID and timestamp ensures uniqueness and supports efficient queries for a specific user's logs. This design prevents the sequential timestamp from creating a single hot node, which is critical for handling millions of writes per second.
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.
- ✗
timestamp_millis#hash(userID)
Why it's wrong here
Timestamp prefix again causes hotspotting.
- ✗
timestamp_millis#userID
Why it's wrong here
Timestamp prefix causes hotspotting; all current writes go to one tablet server.
- ✗
userID#timestamp_millis
Why it's wrong here
Plain userID as prefix can still hotspot if a few users generate most traffic.
- ✓
hash(userID)#userID#timestamp_millis
Why this is correct
Hashing the userID distributes writes evenly. Including userID and timestamp enables efficient queries per user over time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that placing the most selective or unique field first (like timestamp) is best for queries, but in Bigtable the row key design must prioritize write distribution over read optimization to avoid hotspotting.
Detailed technical explanation
How to think about this question
Bigtable splits rows into tablets by row key range; a monotonically increasing prefix like a timestamp causes all new writes to land on the last tablet, creating a hotspot. Using a hash prefix (e.g., MD5 or CRC32) ensures even distribution across tablets, and Bigtable's lexicographic ordering allows efficient range scans when the timestamp is appended. In practice, you might use a 4-byte hash of userID to keep the key compact while still achieving uniform load.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
Storing the Data — This question tests Storing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: hash(userID)#userID#timestamp_millis — Option D is best because it uses a hash of the user ID as the row key prefix, which distributes writes across all Bigtable nodes and avoids hotspotting. Appending the user ID and timestamp ensures uniqueness and supports efficient queries for a specific user's logs. This design prevents the sequential timestamp from creating a single hot node, which is critical for handling millions of writes per second.
What should I do if I get this PDE 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 PDE 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 PDE exam.
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