PCDE Practice Question: Define data structures and implement SQL for Business Intelligence
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. 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 company stores sensor data in BigQuery. They have a table 'sensor_readings' with columns: sensor_id, reading_time, value. The table is partitioned by reading_time (hourly) and clustered by sensor_id. A BI query aggregates average value per sensor for the last week. The query still scans many bytes. What is the most likely cause?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Partition granularity is too fine for the query range
Option D is correct because the query scans a full week of data (168 hourly partitions), and each partition must be read entirely even though only a subset of sensors may be active. Hourly partitioning over a 7-day range means the query engine must scan all 168 partitions, which can result in a large number of bytes being processed. Clustering on sensor_id helps within each partition but does not reduce the number of partitions scanned; the fine granularity of hourly partitioning is the primary cause of excessive bytes scanned.
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.
✗
The query uses SELECT * instead of specific columns
Why it's wrong here
Selecting * increases bytes but the question implies aggregation on value only; still, the main issue is partition count.
✗
Clustering on sensor_id is ineffective
Why it's wrong here
Clustering on sensor_id is appropriate for grouping.
✗
The table is not using columnar storage
Why it's wrong here
BigQuery is columnar by default.
✓
Partition granularity is too fine for the query range
Why this is correct
Hourly partitions for a week means 168 partitions scanned; coarser partitioning (daily) would scan 7 partitions, reducing bytes.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that clustering alone solves all performance issues, but the trap here is that clustering only helps when the query filters or aggregates on the clustered column—without such a filter, clustering does not reduce bytes scanned, and overly fine partitioning is the real culprit.
Detailed technical explanation
How to think about this question
BigQuery’s partitioning pruning works by eliminating entire partitions based on the WHERE clause; with hourly partitions, a 7-day range includes 168 partitions, and each partition’s metadata must be read. Clustering sorts data within each partition by sensor_id, but since the query does not filter on sensor_id, the clustering key is not leveraged for partition elimination or block pruning. In practice, for time-range queries spanning many hours, coarser partitioning (e.g., daily) would reduce the number of partitions scanned and thus the bytes processed, while clustering on sensor_id would still help if a sensor filter were applied.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition granularity is too fine for the query range — Option D is correct because the query scans a full week of data (168 hourly partitions), and each partition must be read entirely even though only a subset of sensors may be active. Hourly partitioning over a 7-day range means the query engine must scan all 168 partitions, which can result in a large number of bytes being processed. Clustering on sensor_id helps within each partition but does not reduce the number of partitions scanned; the fine granularity of hourly partitioning is the primary cause of excessive bytes scanned.
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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