- A
Create a materialized view that pre-aggregates the data by day.
Why wrong: If the query uses the materialized view, it may reduce scans, but the original query still scans the full table unless rewritten.
- B
Add a WHERE clause that filters on the date column used for partitioning.
This enables partition pruning, so only the last 7 days' partitions are scanned, reducing bytes from 1 TB to ~19 GB (1/52).
- C
Cluster the fact table on the join key used in the query.
Why wrong: Clustering improves scan efficiency within partitions but does not reduce bytes scanned if the entire table is still scanned.
- D
Change the table to use time-unit column partitioning with a 1-day partition interval.
Why wrong: The table is already partitioned by date. The optimization is to filter on that column, not repartition.
PDE Maintaining and Automating Data Workloads Practice Question
This PDE practice question tests your understanding of maintaining and automating data workloads. 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.
You are optimizing a BigQuery query that scans 1 TB of data every day. The query joins a large fact table (partitioned by date) with a small dimension table. You notice that the query always scans the entire fact table, even though you only need the last 7 days of data. Which optimization will MOST reduce the bytes scanned?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"always"Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
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
Add a WHERE clause that filters on the date column used for partitioning.
Using a WHERE clause on the partitioning column (e.g., date) allows BigQuery to perform partition pruning, drastically reducing scanned bytes. Clustering on the join key can improve performance but does not reduce bytes scanned as much as partition pruning. Materialized views precompute aggregations but may not reduce scans if the base query still scans full table. Changing to a clustered table does not prune partitions automatically.
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.
- ✗
Create a materialized view that pre-aggregates the data by day.
Why it's wrong here
If the query uses the materialized view, it may reduce scans, but the original query still scans the full table unless rewritten.
- ✓
Add a WHERE clause that filters on the date column used for partitioning.
Why this is correct
This enables partition pruning, so only the last 7 days' partitions are scanned, reducing bytes from 1 TB to ~19 GB (1/52).
Clue confirmation
The clue word "always" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cluster the fact table on the join key used in the query.
Why it's wrong here
Clustering improves scan efficiency within partitions but does not reduce bytes scanned if the entire table is still scanned.
- ✗
Change the table to use time-unit column partitioning with a 1-day partition interval.
Why it's wrong here
The table is already partitioned by date. The optimization is to filter on that column, not repartition.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Maintaining and Automating Data Workloads — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Maintaining and Automating Data Workloads — This question tests Maintaining and Automating Data Workloads — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add a WHERE clause that filters on the date column used for partitioning. — Using a WHERE clause on the partitioning column (e.g., date) allows BigQuery to perform partition pruning, drastically reducing scanned bytes. Clustering on the join key can improve performance but does not reduce bytes scanned as much as partition pruning. Materialized views precompute aggregations but may not reduce scans if the base query still scans full table. Changing to a clustered table does not prune partitions automatically.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "always". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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