The answer is to cluster the fact table on the join key and to use a materialized view to pre-compute the join. Clustering physically co-locates rows with matching join keys, drastically reducing the data scanned and the need for a broadcast join, while a materialized view stores the pre-joined result so BigQuery can read it directly instead of re-broadcasting the dimension table at query time. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of how to avoid excessive shuffle and broadcast costs in large-scale joins, often appearing as a trap where candidates mistakenly choose to increase slot capacity or use a smaller table as the left side. The key insight is that broadcasting a large dimension table repeatedly is the root cause, and both clustering and materialized views eliminate that need by either reducing data locality or caching the result. Memory tip: think “cluster to cut the broadcast, materialize to memorize the join.”
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.
Which TWO optimizations best address slow join performance caused by excessive broadcasting in BigQuery? (Choose two.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 that pre-joins the tables.
Option D is correct because a materialized view can pre-compute and store the join result, eliminating the need to re-execute the join at query time. This avoids the overhead of broadcasting the dimension table repeatedly, as the materialized view is incrementally refreshed and queried directly, reducing both shuffle and broadcast costs.
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.
✗
Use a large query timeout.
Why it's wrong here
Timeout does not optimize join execution.
✗
Set the dimension table to be very large to prevent broadcast.
Why it's wrong here
Larger tables are more likely to be broadcast or cause shuffles.
✗
Increase the number of slots.
Why it's wrong here
Slots improve parallelism but do not eliminate broadcast.
✓
Use a materialized view that pre-joins the tables.
Why this is correct
Materialized views avoid runtime joins.
Clue confirmation
The clue word "best" 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.
Why this is correct
Clustering reduces data movement during join.
Clue confirmation
The clue word "best" 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 misconception that increasing resources (slots or timeout) or making a table larger can fix join performance issues, when the correct approach is to restructure the data or use pre-computed results like materialized views.
Detailed technical explanation
How to think about this question
In BigQuery, broadcast joins occur when one table is small enough to be sent to all slots processing the larger table. Using a materialized view pre-joins the tables and stores the result as a physical table, allowing subsequent queries to read the pre-joined data without re-broadcasting. Clustering the fact table on the join key (Option E) improves join performance by co-locating rows with the same join key, reducing the amount of data that needs to be shuffled or broadcast, especially when combined with a clustered dimension table.
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 that pre-joins the tables. — Option D is correct because a materialized view can pre-compute and store the join result, eliminating the need to re-execute the join at query time. This avoids the overhead of broadcasting the dimension table repeatedly, as the materialized view is incrementally refreshed and queried directly, reducing both shuffle and broadcast costs.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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