- A
Create separate tables for each snapshot_date.
Why wrong: This increases management overhead and does not reduce per-query bytes scanned.
- B
Use clustering on customer_id and snapshot_date.
Why wrong: Clustering helps but queries that scan many dates still process large amounts of data.
- C
Use a nested and repeated structure to store all snapshots per customer in a single row.
Nested fields allow storing an array of snapshots per customer, reducing data scanned per query significantly.
- D
Use a wildcard table with a _TABLE_SUFFIX filter.
Why wrong: Same as B, still need to scan multiple tables if querying multiple dates.
Question 924 of 1,000
PCDE Design and implement database schemas Practice Question
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 data warehouse in BigQuery stores daily snapshots of customer data. The schema uses a single table with a snapshot_date partition column. Over time, the table has grown to 10 TB and queries often scan entire partitions. Which schema redesign would improve query performance and reduce costs significantly?
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
✓
Use a nested and repeated structure to store all snapshots per customer in a single row.
Option C is correct because storing all snapshots per customer in a nested and repeated structure (e.g., an array of structs) eliminates the need to scan multiple rows for the same customer across different snapshot dates. This reduces the table size by avoiding row duplication, and queries that filter on customer_id can leverage the nested structure to read only the relevant data, significantly cutting both query costs (less data scanned) and improving performance.
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.
- ✗
Create separate tables for each snapshot_date.
Why it's wrong here
This increases management overhead and does not reduce per-query bytes scanned.
- ✗
Use clustering on customer_id and snapshot_date.
Why it's wrong here
Clustering helps but queries that scan many dates still process large amounts of data.
- ✓
Use a nested and repeated structure to store all snapshots per customer in a single row.
Why this is correct
Nested fields allow storing an array of snapshots per customer, reducing data scanned per query significantly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a wildcard table with a _TABLE_SUFFIX filter.
Why it's wrong here
Same as B, still need to scan multiple tables if querying multiple dates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake in Google Professional Data Engineer exams is to assume that partitioning or clustering alone solves all performance issues, but for snapshot data with repeated customer records, a nested schema is the most efficient way to reduce data scanned and costs, especially when queries often scan entire partitions.
Detailed technical explanation
How to think about this question
BigQuery's nested and repeated fields (ARRAY<STRUCT>) allow denormalization without data duplication, as each customer row contains an array of snapshot records. This schema leverages BigQuery's columnar storage and can reduce storage costs by eliminating redundant customer_id values across rows. Queries that filter on customer_id can use UNNEST to flatten the array only for matching customers, scanning far fewer bytes than a flat table with millions of rows per customer.
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: Use a nested and repeated structure to store all snapshots per customer in a single row. — Option C is correct because storing all snapshots per customer in a nested and repeated structure (e.g., an array of structs) eliminates the need to scan multiple rows for the same customer across different snapshot dates. This reduces the table size by avoiding row duplication, and queries that filter on customer_id can leverage the nested structure to read only the relevant data, significantly cutting both query costs (less data scanned) and improving performance.
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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