- A
Denormalize user_dimensions into campaign_performance.
Denormalizing adds user_dimension columns to the large table, avoiding the expensive join.
- B
Cluster user_dimensions by user_id.
Why wrong: Clustering reduces shuffle but still requires a join operation.
- C
Partition user_dimensions by date.
Why wrong: Partitioning does not remove the join; it may only help if date filters are applied.
- D
Use a broadcast join hint.
Why wrong: Broadcast join helps but still requires joining; denormalization is more efficient.
Question 474 of 1,000
Define data structures and implement SQL for Business Intelligence →easyMultiple ChoiceObjective-mapped
Avoid Joins in BigQuery by Denormalizing Dimension Tables
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 marketing team uses a BigQuery BI dashboard to analyze campaign performance. The table campaign_performance is 5 TB, partitioned by date, clustered by campaign_id. Queries filter on date range and campaign_id, and are fast. However, one query that joins this table with a user_dimensions table (10 GB, not partitioned) takes too long. The join is on user_id. What is the best improvement?
Quick Answer
The answer is to denormalize user_dimensions into campaign_performance to avoid joins in BigQuery. This is correct because user_dimensions is only 10 GB compared to the 5 TB campaign_performance table, making it small enough to duplicate its columns directly into the fact table, which eliminates the expensive join operation entirely. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of when denormalization is the most efficient optimization—specifically when a dimension table is relatively small and frequently joined to a large, partitioned fact table. A common trap is to focus on partitioning or clustering the dimension table, but those still require a shuffle and join step, whereas denormalization removes the join altogether. Remember the memory tip: “If the dimension is small, fold it into the fact to avoid the join act.”
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
✓
Denormalize user_dimensions into campaign_performance.
Option A is correct because user_dimensions is only 10 GB, small relative to the 5 TB campaign_performance table. Denormalizing the user_dimensions data into campaign_performance eliminates the expensive join entirely, which is the most impactful improvement since joins on large tables are costly. Option B (partitioning user_dimensions by date) does not help because the join is on user_id, not date. Option C (clustering user_dimensions by user_id) can improve join performance but still requires a full shuffle. Option D (using a broadcast join hint) can help if the small table is broadcast, but the join still occurs; denormalization avoids the join altogether.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Denormalize user_dimensions into campaign_performance.
Why this is correct
Denormalizing adds user_dimension columns to the large table, avoiding the expensive join.
Related concept
CIDR notation defines the prefix length.
- ✗
Cluster user_dimensions by user_id.
Why it's wrong here
Clustering reduces shuffle but still requires a join operation.
- ✗
Partition user_dimensions by date.
Why it's wrong here
Partitioning does not remove the join; it may only help if date filters are applied.
- ✗
Use a broadcast join hint.
Why it's wrong here
Broadcast join helps but still requires joining; denormalization is more efficient.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
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.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related PCDE subnetting questions on CIDR, address ranges, and subnet selection.
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FAQ
Questions learners often ask
What does this PCDE question test?
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Denormalize user_dimensions into campaign_performance. — Option A is correct because user_dimensions is only 10 GB, small relative to the 5 TB campaign_performance table. Denormalizing the user_dimensions data into campaign_performance eliminates the expensive join entirely, which is the most impactful improvement since joins on large tables are costly. Option B (partitioning user_dimensions by date) does not help because the join is on user_id, not date. Option C (clustering user_dimensions by user_id) can improve join performance but still requires a full shuffle. Option D (using a broadcast join hint) can help if the small table is broadcast, but the join still occurs; denormalization avoids the join altogether.
What should I do if I get this PCDE question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related PCDE subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
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Last reviewed: Jun 24, 2026
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