- A
Creating a materialized view with GROUP BY region, product, day
Materialized views store precomputed results and are automatically refreshed, reducing query cost and time.
- B
Using a view that queries the raw data with WHERE clause
Why wrong: A view without materialization still scans the entire raw table (subject to partition pruning), not reducing cost compared to materialized alternatives.
- C
Storing pre-aggregated results in a separate table and updating nightly
Why wrong: While this can work, it adds operational overhead and is not as efficient or automatic as materialized views.
- D
Creating indexes on the raw table
Why wrong: BigQuery does not support traditional indexes; it uses partitioning and clustering for optimization.
- E
Using a clustered table on (region, product) with partition by day
Partitioning enables date pruning; clustering on region and product reduces data scanned for those filters.
Quick Answer
The answer is using a clustered table on (region, product) with partition by day and creating a materialized view for the aggregation query. These two methods are effective for BigQuery cost optimization for aggregation queries because they directly reduce the bytes processed. Partitioning by day limits scans to only the relevant date range, while clustering on (region, product) physically co-locates similar rows, making the GROUP BY operation far more efficient. The materialized view goes a step further by pre-computing and storing the aggregated results, so recurring daily sales summaries are served from the stored output without touching the base table at all. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of how to design cost-efficient schemas for analytical workloads—a common trap is assuming that simply adding more indexes or using a non-clustered table will suffice. Remember the memory tip: "Partition to prune, cluster to sort, materialize to freeze." This trio ensures you pay only for the data you actually need to process.
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 is designing a BigQuery data warehouse for sales analytics. They want to minimize query costs when aggregating daily sales by region and product. Which two methods are effective? (Select TWO).
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Creating a materialized view with GROUP BY region, product, day
Option A is correct because a materialized view in BigQuery pre-computes and stores the results of the GROUP BY query on region, product, and day. When the underlying data changes, the materialized view is incrementally refreshed, so queries that match the view's aggregation are served directly from the stored results, avoiding full table scans and reducing query costs (bytes processed). This is ideal for recurring aggregation patterns like daily sales summaries.
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.
- ✓
Creating a materialized view with GROUP BY region, product, day
Why this is correct
Materialized views store precomputed results and are automatically refreshed, reducing query cost and time.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Using a view that queries the raw data with WHERE clause
Why it's wrong here
A view without materialization still scans the entire raw table (subject to partition pruning), not reducing cost compared to materialized alternatives.
- ✗
Storing pre-aggregated results in a separate table and updating nightly
Why it's wrong here
While this can work, it adds operational overhead and is not as efficient or automatic as materialized views.
- ✗
Creating indexes on the raw table
Why it's wrong here
BigQuery does not support traditional indexes; it uses partitioning and clustering for optimization.
- ✓
Using a clustered table on (region, product) with partition by day
Why this is correct
Partitioning enables date pruning; clustering on region and product reduces data scanned for those filters.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
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 distinction between a view (which is just a saved query) and a materialized view (which stores pre-computed results), leading candidates to incorrectly select Option B as a cost-saving measure.
Detailed technical explanation
How to think about this question
Materialized views in BigQuery use automatic incremental refresh based on the change history of the base table (stored in the table's metadata). They are most effective when the base table is append-only or has predictable update patterns, as full recomputation can be triggered if the base table undergoes DML operations like UPDATE or DELETE. Clustering (Option E) physically co-locates rows with the same cluster key values, reducing the bytes scanned when filtering or grouping by those columns, but it does not pre-compute aggregations like a materialized view does.
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?
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: Creating a materialized view with GROUP BY region, product, day — Option A is correct because a materialized view in BigQuery pre-computes and stores the results of the GROUP BY query on region, product, and day. When the underlying data changes, the materialized view is incrementally refreshed, so queries that match the view's aggregation are served directly from the stored results, avoiding full table scans and reducing query costs (bytes processed). This is ideal for recurring aggregation patterns like daily sales summaries.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on PCDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company uses BigQuery to analyze sales data. They need to create a weekly report showing total sales per product category for the last 4 weeks, but the query is taking too long and exceeding slot resources. The sales table has over 2 billion rows and is partitioned by date. Which design change would most improve query performance and reduce slot consumption?
medium- A.Increase the number of available slots in the reservation.
- ✓ B.Cluster the table by product_category within the existing date partitions.
- C.Create a materialized view that pre-aggregates sales by category and date.
- D.Partition the table by product_category instead of date.
Why B: Option B is correct because clustering the table by product_category within the existing date partitions organizes the data physically so that queries filtering or grouping by product_category can skip irrelevant blocks. This reduces the amount of data scanned and the slot consumption, directly addressing the performance issue without requiring additional resources.
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Last reviewed: Jun 30, 2026
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