The answer is to use materialized views that match common query patterns, along with partitioning and clustering tables. These three actions directly reduce the amount of data processed per query by enabling BigQuery to prune irrelevant data before scanning. Partitioning, typically on a date column, allows queries with a WHERE clause to skip entire partitions, while clustering sorts data within partitions to limit scans to relevant blocks. Materialized views precompute and store results for frequent query patterns, so BigQuery reads only the pre-aggregated output instead of raw tables. On the Google Professional Cloud Database Engineer exam, this question tests your understanding of cost optimization fundamentals, often appearing as a multi-select scenario where a common trap is to choose indexing or caching instead of these three core techniques. Remember the mnemonic “PCM” — Partition, Cluster, Materialize — to recall the trio that cuts data scanned and billed.
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 wants to reduce BigQuery query costs for their BI workloads. Which THREE actions effectively lower the amount of data processed per query? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use partitioned tables on date column
Partitioned tables in BigQuery allow queries to use the WHERE clause to filter on the partition column (e.g., a date column), so BigQuery can prune entire partitions from the scan. This directly reduces the amount of data read and billed, lowering query costs. Option A is correct because it is a primary cost-control mechanism in BigQuery.
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.
✓
Use partitioned tables on date column
Why this is correct
Partitioning limits query scans to relevant partitions, cutting bytes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use LIMIT in subqueries to reduce output
Why it's wrong here
LIMIT does not reduce the scan of the underlying table; it only limits output rows.
✓
Use clustered tables on frequently filtered columns
Why this is correct
Clustering organizes data so that queries scan fewer blocks, reducing bytes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SELECT * to avoid missing columns
Why it's wrong here
SELECT * often scans all columns, increasing bytes.
✓
Use materialized views that match common query patterns
Why this is correct
Materialized views store pre-computed results, so queries read only the aggregated data.
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 row-limiting clauses like LIMIT reduce data processing costs, but in BigQuery, only column and partition pruning reduce the bytes scanned.
Trap categories for this question
Command / output trap
LIMIT does not reduce the scan of the underlying table; it only limits output rows.
Detailed technical explanation
How to think about this question
BigQuery charges based on the number of bytes read from storage, not the number of rows returned. Partition pruning works by using the table's metadata to skip entire storage blocks (e.g., day-level partitions), while clustering sorts data within partitions, enabling block-level pruning for filtered columns. In real-world BI workloads, combining partitioning on a date column with clustering on frequently filtered dimensions (e.g., region, product) can reduce costs by 90% or more compared to unoptimized tables.
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
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: Use partitioned tables on date column — Partitioned tables in BigQuery allow queries to use the WHERE clause to filter on the partition column (e.g., a date column), so BigQuery can prune entire partitions from the scan. This directly reduces the amount of data read and billed, lowering query costs. Option A is correct because it is a primary cost-control mechanism in BigQuery.
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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Question Discussion
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