- 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.
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.”
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.
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?
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 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 C is correct because user_dimensions is small (10 GB) relative to campaign_performance, denormalizing eliminates the join entirely. Option A (partition user_dimensions) helps but the join still occurs. Option B (cluster by user_id) reduces shuffle but not elimination. Option D (broadcast join hint) forces a broadcast but join still occurs.
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
- →
Define data structures and implement SQL for Business Intelligence — study guide chapter
Learn the concepts, then practise the questions
- →
Define data structures and implement SQL for Business Intelligence practice questions
Targeted practice on this topic area only
- →
All PCDE questions
503 questions across all exam domains
- →
Google Professional Cloud Database Engineer study guide
Full concept coverage aligned to exam objectives
- →
PCDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PCDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Plan and manage database infrastructure practice questions
Practise PCDE questions linked to Plan and manage database infrastructure.
Define data structures and implement SQL for Business Intelligence practice questions
Practise PCDE questions linked to Define data structures and implement SQL for Business Intelligence.
Design and implement database schemas practice questions
Practise PCDE questions linked to Design and implement database schemas.
Monitor and optimize database performance practice questions
Practise PCDE questions linked to Monitor and optimize database performance.
PCDE fundamentals practice questions
Practise PCDE questions linked to PCDE fundamentals.
PCDE scenario practice questions
Practise PCDE questions linked to PCDE scenario.
PCDE troubleshooting practice questions
Practise PCDE questions linked to PCDE troubleshooting.
Practice this exam
Start a free PCDE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 C is correct because user_dimensions is small (10 GB) relative to campaign_performance, denormalizing eliminates the join entirely. Option A (partition user_dimensions) helps but the join still occurs. Option B (cluster by user_id) reduces shuffle but not elimination. Option D (broadcast join hint) forces a broadcast but join still occurs.
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.
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?
CIDR notation defines the prefix length.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 24, 2026
This PCDE 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 PCDE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.