- A
Clustering does not create indexes on symbol
Why wrong: BigQuery does not use indexes; clustering organizes storage but does not guarantee fast lookup for a single symbol.
- B
Clustering on symbol may cause many blocks to be scanned because symbols are not sorted
If data is ingested without sorting by symbol, clustering effectiveness decreases, leading to many blocks being scanned.
- C
Partitioning causes data skew across partitions
Why wrong: Partitioning by date typically results in even distribution.
- D
Partitioning by date is not granular enough
Why wrong: Date partitioning is appropriate for 30-day window functions.
Question 54 of 1,000
Define data structures and implement SQL for Business Intelligence →hardMultiple ChoiceObjective-mapped
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial services company uses BigQuery for risk analysis. They have a table `market_data` with columns `symbol`, `date`, `price`, and `volume`. The query pattern involves window functions over the last 30 days for many symbols. The table is partitioned by date and clustered by symbol. However, analysts report that queries are slow and expensive. 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 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
✓
Clustering on symbol may cause many blocks to be scanned because symbols are not sorted
Option B is correct. BigQuery clustering does physically sort data by symbol within each partition. However, for a query using window functions over a rolling 30-day window across many symbols, the sorting alone does not reduce the number of blocks scanned. Because the table is partitioned by date, the window function must read data from multiple partitions, and clustering by a high-cardinality column like symbol still requires scanning many blocks since each block contains many distinct symbols. The option's wording 'symbols are not sorted' is inaccurate; the real issue is that clustering does not provide efficient pruning for range-based window functions across partitions.
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.
- ✗
Clustering does not create indexes on symbol
Why it's wrong here
BigQuery does not use indexes; clustering organizes storage but does not guarantee fast lookup for a single symbol.
- ✓
Clustering on symbol may cause many blocks to be scanned because symbols are not sorted
Why this is correct
If data is ingested without sorting by symbol, clustering effectiveness decreases, leading to many blocks being scanned.
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.
- ✗
Partitioning causes data skew across partitions
Why it's wrong here
Partitioning by date typically results in even distribution.
- ✗
Partitioning by date is not granular enough
Why it's wrong here
Date partitioning is appropriate for 30-day window functions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume clustering works like an index or a sort order, but BigQuery clustering only co-locates similar values without guaranteeing strict ordering, which leads to inefficient block pruning for range-based queries over high-cardinality columns.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery clustering uses a column-based storage format where each block stores a min/max range of cluster column values. For window functions over a 30-day window, BigQuery must scan all blocks that overlap with the query's date range and symbol set. If symbols are not sorted, a single block may contain many different symbols, causing the query to scan far more blocks than necessary. In real-world scenarios, analysts often see cost spikes when clustering is applied to high-cardinality columns without sorting, as each block's pruning efficiency drops dramatically.
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
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 PCDE question test?
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: Clustering on symbol may cause many blocks to be scanned because symbols are not sorted — Option B is correct. BigQuery clustering does physically sort data by symbol within each partition. However, for a query using window functions over a rolling 30-day window across many symbols, the sorting alone does not reduce the number of blocks scanned. Because the table is partitioned by date, the window function must read data from multiple partitions, and clustering by a high-cardinality column like symbol still requires scanning many blocks since each block contains many distinct symbols. The option's wording 'symbols are not sorted' is inaccurate; the real issue is that clustering does not provide efficient pruning for range-based window functions across partitions.
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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Last reviewed: Jun 11, 2026
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