- A
Add a covering index on (product_id, quantity).
Why wrong: Even a covering index would still require scanning all rows for a product.
- B
Migrate the inventory table to Cloud Spanner and use interleaved indexes.
Why wrong: This is a database migration, not a schema redesign within Cloud SQL.
- C
Use BigQuery as a read replica and query there.
Why wrong: This is an external system, not a schema design change.
- D
Create a summary table 'product_totals' with columns product_id and total_quantity, and use triggers to keep it updated on INSERT/UPDATE/DELETE in inventory.
Pre-aggregation reduces the amount of work needed at query time.
Question 824 of 1,000
PCDE Summary table Practice Question
This PCDE practice question tests your understanding of design and implement database schemas. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: summary table. 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 retail company uses Cloud SQL for PostgreSQL for inventory management. The schema has a table 'inventory' with columns: product_id, warehouse_id, quantity, last_updated. The table contains over 100 million rows. The application frequently runs aggregate queries to compute total quantity of a product across all warehouses (e.g., SELECT SUM(quantity) FROM inventory WHERE product_id = ?). These queries are slow, taking tens of seconds. The team tries a covering index on (product_id, quantity) but sees little improvement because they still need to scan many rows. They need to redesign the schema to improve aggregation performance. What is the best approach?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Create a summary table 'product_totals' with columns product_id and total_quantity, and use triggers to keep it updated on INSERT/UPDATE/DELETE in inventory.
Option D is correct. Creating a summary table 'product_totals' that pre-aggregates total quantity per product, updated via triggers on INSERT, UPDATE, DELETE in the inventory table, dramatically speeds up aggregate queries by avoiding full scans of the large table. Option A (covering index on product_id, quantity) was tried and still requires scanning many rows to sum quantities, so it does not solve the performance issue. Option B (migrating to Cloud Spanner) is an unnecessary and costly migration. Option C (using BigQuery as a read replica) adds latency and complexity without being a schema redesign. Thus, D is the best approach.
Key principle: Summary table
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a covering index on (product_id, quantity).
Why it's wrong here
Even a covering index would still require scanning all rows for a product.
- ✗
Migrate the inventory table to Cloud Spanner and use interleaved indexes.
Why it's wrong here
This is a database migration, not a schema redesign within Cloud SQL.
- ✗
Use BigQuery as a read replica and query there.
Why it's wrong here
This is an external system, not a schema design change.
- ✓
Create a summary table 'product_totals' with columns product_id and total_quantity, and use triggers to keep it updated on INSERT/UPDATE/DELETE in inventory.
Why this is correct
Pre-aggregation reduces the amount of work needed at query time.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Summary table
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates might assume that a covering index or a materialized view would be sufficient. However, Cloud SQL for PostgreSQL does not support materialized views with automatic refresh for this pattern, and a covering index still requires scanning all rows per product. The correct schema redesign is a summary table with triggers.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Summary table
- Database trigger
- Covering index
- Pre-aggregation
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
Summary table
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.
Review summary table, then practise related PCDE questions on the same topic to reinforce the concept.
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Design and implement database schemas — study guide chapter
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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 — Summary table.
What is the correct answer to this question?
The correct answer is: Create a summary table 'product_totals' with columns product_id and total_quantity, and use triggers to keep it updated on INSERT/UPDATE/DELETE in inventory. — Option D is correct. Creating a summary table 'product_totals' that pre-aggregates total quantity per product, updated via triggers on INSERT, UPDATE, DELETE in the inventory table, dramatically speeds up aggregate queries by avoiding full scans of the large table. Option A (covering index on product_id, quantity) was tried and still requires scanning many rows to sum quantities, so it does not solve the performance issue. Option B (migrating to Cloud Spanner) is an unnecessary and costly migration. Option C (using BigQuery as a read replica) adds latency and complexity without being a schema redesign. Thus, D is the best approach.
What should I do if I get this PCDE question wrong?
Review summary table, 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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Summary table
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Last reviewed: Jun 24, 2026
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