- A
The query does not include a filter on the clustering column, so clustering provides no benefit.
Why wrong: Clustering benefits aggregation even without a filter, so this is not the primary cause.
- B
The query uses a LEFT JOIN that requires a broadcast join, increasing network overhead.
Why wrong: No join is mentioned in the scenario.
- C
The query filters on a date column that is not the partition column, causing a full table scan.
Partition pruning only works when the filter is on the partition column; otherwise, all partitions are scanned.
- D
The table does not have a primary key, so BigQuery cannot use index scans.
Why wrong: BigQuery does not use primary keys or index scans; it relies on columnar storage and partitioning.
Question 246 of 1,000
Define data structures and implement SQL for Business Intelligence →mediumMultiple ChoiceObjective-mapped
PCDE Partition Pruning Practice Question
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: partition Pruning. 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 analyst reports that a BI dashboard query on BigQuery is taking over 30 seconds to execute. The table is partitioned by date and clustered by customer_id. The query filters on a specific date range and aggregates sales by customer. What is the most likely cause of the slow performance?
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 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
✓
The query filters on a date column that is not the partition column, causing a full table scan.
Option C is correct because the query is slow due to filtering on a date column that is not the partition column. Even though the table is partitioned by date, if the WHERE clause uses a different date column, BigQuery cannot perform partition pruning and must scan all partitions, leading to high latency. Option A is incorrect because the main issue is partition pruning, not clustering benefits. Option B is incorrect as no join is mentioned in the question. Option D is incorrect because BigQuery does not use primary keys or indexes.
Key principle: Partition Pruning
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 does not include a filter on the clustering column, so clustering provides no benefit.
Why it's wrong here
Clustering benefits aggregation even without a filter, so this is not the primary cause.
- ✗
The query uses a LEFT JOIN that requires a broadcast join, increasing network overhead.
Why it's wrong here
No join is mentioned in the scenario.
- ✓
The query filters on a date column that is not the partition column, causing a full table scan.
Why this is correct
Partition pruning only works when the filter is on the partition column; otherwise, all partitions are scanned.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Partition Pruning
- ✗
The table does not have a primary key, so BigQuery cannot use index scans.
Why it's wrong here
BigQuery does not use primary keys or index scans; it relies on columnar storage and partitioning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is assuming that any date filter triggers partition pruning. In BigQuery, only filters on the partition column enable pruning. If the date column in the filter is not the partition column, a full table scan occurs despite partitioning being defined on another date column.
Trap categories for this question
Scenario analysis trap
No join is mentioned in the scenario.
Detailed technical explanation
How to think about this question
BigQuery uses a columnar storage format (Capacitor) and leverages partitioning to eliminate entire storage blocks during query execution via partition pruning. Clustering further organizes data within partitions based on the clustering column, but it only accelerates queries that filter or aggregate on that column. In this scenario, the query filters on a date range, so partition pruning reduces the data scanned, but without a filter on customer_id, clustering provides no benefit, and the query may still scan all rows in the relevant partitions, leading to slow performance if the date range is broad.
KKey Concepts to Remember
- Partition Pruning
- Clustering
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
Partition Pruning
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Review partition Pruning, then practise related PCDE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PCDE question test?
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Partition Pruning.
What is the correct answer to this question?
The correct answer is: The query filters on a date column that is not the partition column, causing a full table scan. — Option C is correct because the query is slow due to filtering on a date column that is not the partition column. Even though the table is partitioned by date, if the WHERE clause uses a different date column, BigQuery cannot perform partition pruning and must scan all partitions, leading to high latency. Option A is incorrect because the main issue is partition pruning, not clustering benefits. Option B is incorrect as no join is mentioned in the question. Option D is incorrect because BigQuery does not use primary keys or indexes.
What should I do if I get this PCDE question wrong?
Review partition Pruning, then practise related PCDE questions on the same topic to reinforce the concept.
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?
Partition Pruning
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Last reviewed: Jun 25, 2026
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