The answer is partitioning the table by activity_date and using a materialized view to precompute COUNT DISTINCT user_id per day. This works because BigQuery’s on-demand pricing charges based on the bytes processed, and a materialized view stores the pre-aggregated result of the distinct count, so queries against it read only the small, precomputed output instead of scanning the entire base table. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of cost optimization through schema design and materialization, often appearing as a two-part optimization where partitioning reduces the scan range and the materialized view eliminates repeated full-table distinct computations. A common trap is choosing only partitioning or only clustering, but the real cost savings come from combining both—partitioning limits the data read, and the materialized view avoids the expensive distinct scan. Memory tip: “Partition to prune, materialize to summarize.”
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 create a BI dashboard that shows daily active users. The data is stored in a BigQuery table with columns: user_id, activity_date, and event_type. Which two optimizations would help reduce query costs? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a materialized view with COUNT(DISTINCT user_id) grouped by activity_date.
Option C is correct because a materialized view precomputes the COUNT(DISTINCT user_id) grouped by activity_date, so queries against it read only the pre-aggregated results rather than scanning the entire base table. This drastically reduces the amount of data processed, lowering query costs in BigQuery's on-demand pricing model where cost is proportional to bytes processed.
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.
✗
Cluster the table by event_type.
Why it's wrong here
Clustering on event_type may not help since the query aggregates by user across events.
✗
Use SELECT * and filter in the BI tool.
Why it's wrong here
SELECT * scans all columns, increasing costs.
✓
Use a materialized view with COUNT(DISTINCT user_id) grouped by activity_date.
Why this is correct
A materialized view caches the aggregation, avoiding repeated computation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Avoid using the LIMIT clause.
Why it's wrong here
LIMIT does not affect cost; it only affects returned rows.
✓
Partition the table by activity_date.
Why this is correct
Partitioning allows pruning to relevant date ranges, reducing scanned 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 clustering alone reduces query cost for any aggregation, but clustering only reduces cost when the query filters or groups by the cluster key, not when the aggregation is on a different column like activity_date.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery's materialized views are automatically and incrementally updated as the base table changes, using change tracking to avoid full recomputation. In a real-world scenario with a high-cardinality user_id column, COUNT(DISTINCT) can be expensive to compute on the fly, but a materialized view stores the exact distinct count per day, making daily active user queries nearly instant and cost-free beyond the storage of the view itself.
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 a materialized view with COUNT(DISTINCT user_id) grouped by activity_date. — Option C is correct because a materialized view precomputes the COUNT(DISTINCT user_id) grouped by activity_date, so queries against it read only the pre-aggregated results rather than scanning the entire base table. This drastically reduces the amount of data processed, lowering query costs in BigQuery's on-demand pricing model where cost is proportional to bytes processed.
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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