- A
Partition by ingestion_time and cluster by sensor_id.
Why wrong: Ingestion time may not correspond to event time, and writes are append-only, causing hotspotting.
- B
Use integer range partitioning on sensor_id.
Why wrong: Integer range partitioning is not optimal for date-based queries.
- C
Partition by date and cluster by sensor_id with a timestamp column.
Date-based partitioning efficiently prunes scans; clustering by sensor_id further reduces data read.
- D
Partition by sensor_id and cluster by timestamp.
Why wrong: Partitioning by sensor_id creates many small partitions, increasing cost and management.
Question 68 of 1,000
BigQuery Partitioning and Clustering for IoT Time-Series
This PCDE practice question tests your understanding of design and implement database schemas. 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 team is designing a BigQuery schema for time-series analytics on IoT sensor data. They expect high write throughput and queries that aggregate data by hour. Which partitioning and clustering strategy is most cost-effective?
Quick Answer
The answer is to partition by date and cluster by sensor_id with a timestamp column. This strategy is most cost-effective for IoT time-series analytics because partitioning by date (using ingestion or event time) automatically prunes irrelevant data for hourly aggregation queries, while clustering by sensor_id physically co-locates rows for the same sensor, drastically reducing the data scanned when filtering on specific devices. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding that partitioning should align with the primary query filter (time range), while clustering optimizes secondary filters like sensor_id. A common trap is choosing partition by sensor_id, which would create too many small partitions and degrade write throughput, or using integer range partitioning for dates, which lacks the automatic time-based pruning. Memory tip: partition the time, cluster the thing.
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
✓
Partition by date and cluster by sensor_id with a timestamp column.
Option C is correct because partitioning by date (e.g., _PARTITIONDATE) allows BigQuery to prune partitions when querying hourly aggregates, drastically reducing scanned bytes. Clustering by sensor_id with a timestamp column further organizes data within each partition, enabling efficient filtering and sorting for sensor-specific queries. This combination optimizes both write throughput (partitioning avoids small, frequent partitions) and query performance (clustering reduces scan on sensor_id filters).
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.
- ✗
Partition by ingestion_time and cluster by sensor_id.
Why it's wrong here
Ingestion time may not correspond to event time, and writes are append-only, causing hotspotting.
- ✗
Use integer range partitioning on sensor_id.
Why it's wrong here
Integer range partitioning is not optimal for date-based queries.
- ✓
Partition by date and cluster by sensor_id with a timestamp column.
Why this is correct
Date-based partitioning efficiently prunes scans; clustering by sensor_id further reduces data read.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Partition by sensor_id and cluster by timestamp.
Why it's wrong here
Partitioning by sensor_id creates many small partitions, increasing cost and management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the roles of partitioning and clustering. A common mistake is to partition on a high-cardinality column like sensor_id, which BigQuery limits to 4,000 partitions, causing partition explosion and degraded performance. Understanding this BigQuery constraint is key to choosing the right strategy.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery's columnar storage and clustering sort data within each partition based on the clustering columns, allowing it to use block-level metadata to skip entire blocks that don't match filter criteria. For hourly aggregation queries, partitioning by date ensures that only the relevant day's partitions are scanned, and clustering by sensor_id with a timestamp column further narrows the scan to specific sensor blocks within that partition. A real-world scenario: a fleet of 10,000 sensors reporting every minute would generate 14.4M rows per day; partitioning by date keeps each partition manageable (~14.4M rows), while clustering by sensor_id allows queries for a single sensor to scan only ~1,440 rows per day.
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 PCDE question test?
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: Partition by date and cluster by sensor_id with a timestamp column. — Option C is correct because partitioning by date (e.g., _PARTITIONDATE) allows BigQuery to prune partitions when querying hourly aggregates, drastically reducing scanned bytes. Clustering by sensor_id with a timestamp column further organizes data within each partition, enabling efficient filtering and sorting for sensor-specific queries. This combination optimizes both write throughput (partitioning avoids small, frequent partitions) and query performance (clustering reduces scan on sensor_id filters).
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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