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.
An e-commerce company uses BigQuery for BI. They have a large orders table with columns: order_id, customer_id, order_date, amount, status. Queries frequently aggregate total amount by customer and month. The current table is not partitioned. Users complain about high costs. The table is 2 TB and grows by 50 GB daily. Which action reduces query costs most?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a materialized view that aggregates by customer and month.
Option C is correct because a materialized view pre-aggregates the total amount by customer and month, eliminating the need to scan the full 2 TB table for every query. This drastically reduces the bytes processed per query, directly lowering BigQuery costs. Since the table grows by 50 GB daily, the materialized view incrementally updates, ensuring fresh results without reprocessing historical data.
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.
✗
Partition the table by order_date and cluster by customer_id.
Why it's wrong here
Without a WHERE clause on date, queries still scan all partitions; clustering helps filtering but not full aggregation.
✗
Use a wildcard table with daily shards.
Why it's wrong here
Wildcard tables are deprecated; they don't reduce costs compared to partitioned tables.
✓
Create a materialized view that aggregates by customer and month.
Why this is correct
Materialized view stores the aggregation, converting queries to small scans of precomputed data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set a maximum bytes billed limit on the project.
Why it's wrong here
Max bytes billed only prevents costly queries; it does not reduce cost for allowed queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that partitioning alone solves all cost issues, but the trap here is that partitioning reduces scan for date-range queries, not for aggregation queries that span many partitions; a materialized view is the correct cost-reduction strategy for pre-aggregated results.
Detailed technical explanation
How to think about this question
BigQuery materialized views are automatically maintained and use the table's base data to compute incremental changes via change tracking. When the base table is updated, BigQuery only processes the delta (new rows) to refresh the view, making it highly efficient for append-heavy tables like this 50 GB/day growth scenario. Under the hood, the materialized view stores pre-joined and pre-aggregated results in a separate, optimized storage layer, allowing queries to read only the aggregated output rather than scanning raw rows.
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: Create a materialized view that aggregates by customer and month. — Option C is correct because a materialized view pre-aggregates the total amount by customer and month, eliminating the need to scan the full 2 TB table for every query. This drastically reduces the bytes processed per query, directly lowering BigQuery costs. Since the table grows by 50 GB daily, the materialized view incrementally updates, ensuring fresh results without reprocessing historical data.
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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