- A
Set the maximum QPS to 1000 to avoid overloading the cluster.
Why wrong: Bigtable can handle millions of QPS; limiting QPS is not a design choice for taming load.
- B
Partition the data into separate tables for each month.
Why wrong: Multiple tables increase administrative complexity and do not improve read latency.
- C
Use a reverse timestamp in the row key so that recent data is at the beginning of the table.
Reversing the timestamp makes the most recent data appear first, speeding up reads for the last hour.
- D
Use a salting prefix on the row key to distribute writes across nodes.
Salting prevents hotspotting by distributing writes across multiple tablet servers.
- E
Design row keys to be as long as possible to avoid collisions.
Why wrong: Short row keys are preferred; long keys waste storage and memory.
Bigtable Row Key Design for Time-Series Data: Salting and Reverse Timestamps
This PCDE practice question tests your understanding of plan and manage database infrastructure. 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 manufacturing company is deploying a time-series database for sensor data on Cloud Bigtable. They expect 100 TB of data per year and need low-latency reads on row keys within the last hour. Which TWO design choices should they make?
Quick Answer
The correct answer is to use a salting prefix on the row key and reverse timestamps for the row key design. Salting distributes write load across Bigtable nodes by adding a hash prefix, preventing hotspotting when thousands of sensors write simultaneously. Reverse timestamps (e.g., `[salt][max_timestamp - timestamp]`) ensure that the most recent sensor readings appear first in lexicographic order, enabling low-latency scans for the last hour of data. On the Google Professional Cloud Database Engineer exam, this question tests your understanding of Bigtable’s key design principles for time-series workloads—specifically how to balance write throughput with read efficiency. A common trap is choosing to split data across multiple tables, which adds management overhead without solving hotspotting, or making row keys too long, which wastes memory. Remember the mnemonic: “Salt to scatter, reverse to recent.”
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
Use a reverse timestamp in the row key so that recent data is at the beginning of the table.
Option C is correct because using a reverse timestamp in the row key (e.g., `[max_timestamp - timestamp]`) ensures that the most recent sensor data appears at the beginning of the sorted Bigtable row range. This allows low-latency scans for the last hour's data without scanning the entire table, as Bigtable stores rows in lexicographic order by row key.
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.
- ✗
Set the maximum QPS to 1000 to avoid overloading the cluster.
Why it's wrong here
Bigtable can handle millions of QPS; limiting QPS is not a design choice for taming load.
- ✗
Partition the data into separate tables for each month.
Why it's wrong here
Multiple tables increase administrative complexity and do not improve read latency.
- ✓
Use a reverse timestamp in the row key so that recent data is at the beginning of the table.
Why this is correct
Reversing the timestamp makes the most recent data appear first, speeding up reads for the last hour.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a salting prefix on the row key to distribute writes across nodes.
Why this is correct
Salting prevents hotspotting by distributing writes across multiple tablet servers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Design row keys to be as long as possible to avoid collisions.
Why it's wrong here
Short row keys are preferred; long keys waste storage and memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google PCDE exam often tests the misconception that partitioning data into separate tables (option B) is a good scaling strategy, when in fact Bigtable's single-table design with proper row key structure is the correct approach for time-series data.
Detailed technical explanation
How to think about this question
Bigtable stores rows in sorted order by row key, so a reverse timestamp (e.g., `Long.MAX_VALUE - timestamp`) places the newest data at the start of the table, enabling efficient prefix scans for recent data. The salting prefix (option D) distributes writes across tablet servers by adding a hash prefix to the row key, preventing hot-spotting on a single node when many writes target the same timestamp range. In practice, a common pattern is to combine a salting prefix (e.g., a hash of the sensor ID) with a reverse timestamp to achieve both write distribution and efficient recent-data reads.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this PCDE question test?
Plan and manage database infrastructure — This question tests Plan and manage database infrastructure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a reverse timestamp in the row key so that recent data is at the beginning of the table. — Option C is correct because using a reverse timestamp in the row key (e.g., `[max_timestamp - timestamp]`) ensures that the most recent sensor data appears at the beginning of the sorted Bigtable row range. This allows low-latency scans for the last hour's data without scanning the entire table, as Bigtable stores rows in lexicographic order by row key.
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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Last reviewed: Jul 4, 2026
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