The answer is to denormalize some tables by merging fact and dimension tables. This technique directly accelerates analytical queries by reducing the number of expensive joins required in a highly normalized snowflake schema, which is the root cause of the performance bottleneck. By storing redundant data in a star-like schema, the data warehouse prioritizes read speed for complex aggregations over write efficiency, a common trade-off that still preserves data integrity through careful ETL design. On the CompTIA ITF+ FC0-U61 exam, this scenario tests your understanding of how schema design impacts query performance in data warehousing, often appearing in questions about balancing normalization against reporting speed. A common trap is assuming full denormalization is always best, but the correct approach is selective denormalization of heavily joined tables. Remember the memory tip: “Snowflake slows, star speeds—merge the facts to cut the joins.”
FC0-U61 Database Fundamentals Practice Question
This FC0-U61 practice question tests your understanding of database fundamentals. 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 large retail chain operates a data warehouse that combines sales data from multiple source databases. The warehouse is designed using a highly normalized snowflake schema. Analysts frequently run complex queries that aggregate sales across many dimensions (e.g., time, product, store). Recently, the queries have become very slow, often taking hours to complete. The data warehouse team suspects the normalization is causing many joins, degrading performance. The business users need faster reporting. The team must decide on a course of action that balances query performance with maintainability. Which technique is most likely to improve reporting speed without significantly compromising data integrity?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Denormalize some tables by merging fact and dimension tables
Denormalizing some tables by merging fact and dimension tables reduces the number of joins required for complex analytical queries, directly addressing the performance bottleneck caused by the highly normalized snowflake schema. This technique improves query speed by storing redundant data in a star-like schema, which is a common optimization for data warehouses where read performance is prioritized over write efficiency, while still maintaining data integrity through careful design and ETL processes.
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.
✓
Denormalize some tables by merging fact and dimension tables
Why this is correct
Reduces joins, improving read performance for aggregations.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the server's CPU and memory resources
Why it's wrong here
Hardware upgrade is a band-aid; the root cause is schema design.
✗
Replace the relational warehouse with a NoSQL document store
Why it's wrong here
NoSQL may not support complex analytic queries and requires major rework.
✗
Add more indexes on all foreign key columns
Why it's wrong here
Indexes help but cannot fully compensate for excessive joins.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that adding more indexes always improves query performance, but in a highly normalized schema with many joins, the overhead of maintaining and scanning multiple indexes can actually slow down complex aggregations.
Detailed technical explanation
How to think about this question
In a snowflake schema, dimension tables are further normalized into sub-dimensions, leading to many-to-one relationships that require multiple joins per query. Denormalization into a star schema collapses these sub-dimensions into single tables, reducing join depth and allowing the database optimizer to use simpler hash joins or even full table scans more efficiently. Real-world data warehouses like Amazon Redshift or Snowflake often recommend star schemas for OLAP workloads because they balance query speed with manageable ETL complexity, and denormalization can be applied selectively to the most frequently queried dimensions (e.g., time and product) to avoid excessive data duplication.
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 practitioner preparing for the FC0-U61 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
Database Fundamentals — This question tests Database Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Denormalize some tables by merging fact and dimension tables — Denormalizing some tables by merging fact and dimension tables reduces the number of joins required for complex analytical queries, directly addressing the performance bottleneck caused by the highly normalized snowflake schema. This technique improves query speed by storing redundant data in a star-like schema, which is a common optimization for data warehouses where read performance is prioritized over write efficiency, while still maintaining data integrity through careful design and ETL processes.
What should I do if I get this FC0-U61 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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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