- A
BigQuery table clustering on the date column
Why wrong: Clustering sorts data within each partition by column values — it helps with filter pruning within a partition but doesn't create partition-level boundaries. Partitioning is needed to eliminate whole date ranges.
- B
Creating a materialized view for the last 30 days
Why wrong: Materialized views cache query results and auto-refresh — useful for frequently run queries, but don't reduce scanning on the base table for ad-hoc date-range queries.
- C
Date/timestamp partitioned table on the date column
Partitioning by date divides the table into daily (or monthly) segments. Queries with date range filters only scan the relevant partitions, avoiding full table scans and reducing cost.
- D
Increasing BigQuery slot reservations for faster full-table scans
Why wrong: More slots increase query speed by parallelizing work, but don't reduce the amount of data scanned — costs remain the same and full-table scans still occur.
Quick Answer
The answer is a date/timestamp partitioned table on the date column. This is correct because BigQuery partitioning physically separates data into segments based on the partition column, allowing the query engine to perform partition pruning—it reads only the partitions that match the date range filter, such as the last 30 days, instead of scanning the full 10 TB table. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of cost optimization through data skipping; a common trap is confusing clustering (which sorts data within a partition but does not eliminate partition scans) with partitioning. For date range queries, partitioning is the direct feature that reduces bytes billed. Remember the memory tip: “Partition to prune, cluster to sort”—if your query filters by a date column, always partition by that column to avoid paying for irrelevant data.
Google ACE Planning and configuring a cloud solution Practice Question
This ACE practice question tests your understanding of planning and configuring a cloud solution. 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 team queries a 10 TB BigQuery table containing billions of events with a date column. Most queries filter by a date range (e.g., last 30 days). Without any partitioning, queries scan the full 10 TB every time. Which BigQuery feature eliminates unnecessary data scanning for date-range queries?
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
Date/timestamp partitioned table on the date column
Partitioning a BigQuery table by the date column allows the query engine to prune entire partitions that fall outside the specified date range, so only the relevant partitions (e.g., last 30 days) are scanned instead of the full 10 TB. This directly reduces data scanned and cost, making option C the correct choice for eliminating unnecessary scanning in date-range queries.
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.
- ✗
BigQuery table clustering on the date column
Why it's wrong here
Clustering sorts data within each partition by column values — it helps with filter pruning within a partition but doesn't create partition-level boundaries. Partitioning is needed to eliminate whole date ranges.
- ✗
Creating a materialized view for the last 30 days
Why it's wrong here
Materialized views cache query results and auto-refresh — useful for frequently run queries, but don't reduce scanning on the base table for ad-hoc date-range queries.
- ✓
Date/timestamp partitioned table on the date column
Why this is correct
Partitioning by date divides the table into daily (or monthly) segments. Queries with date range filters only scan the relevant partitions, avoiding full table scans and reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increasing BigQuery slot reservations for faster full-table scans
Why it's wrong here
More slots increase query speed by parallelizing work, but don't reduce the amount of data scanned — costs remain the same and full-table scans still occur.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between partitioning (physical data separation) and clustering (logical sorting within a table), leading candidates to mistakenly choose clustering as a cost-saving measure when only partitioning actually prunes data at the storage level.
Detailed technical explanation
How to think about this question
BigQuery partitioned tables use a pseudo-column `_PARTITIONTIME` for ingestion-time partitioning or a specified column for unit-time partitioning, enabling partition-level pruning at the storage layer. Under the hood, each partition is stored as a separate set of columnar files in Colossus, and the query planner uses the WHERE clause to skip reading files from irrelevant partitions entirely. A real-world scenario: a 10 TB table with daily partitions for 365 days means a query for the last 30 days scans only ~0.82 TB, reducing costs by over 90%.
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.
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FAQ
Questions learners often ask
What does this ACE question test?
Planning and configuring a cloud solution — This question tests Planning and configuring a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Date/timestamp partitioned table on the date column — Partitioning a BigQuery table by the date column allows the query engine to prune entire partitions that fall outside the specified date range, so only the relevant partitions (e.g., last 30 days) are scanned instead of the full 10 TB. This directly reduces data scanned and cost, making option C the correct choice for eliminating unnecessary scanning in date-range queries.
What should I do if I get this ACE 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: Jun 30, 2026
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