DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. 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 company is designing a data lake on Amazon S3 with Amazon Redshift Spectrum for analytics. The data includes JSON logs from web servers. Which THREE design practices should the company follow to optimize query performance and cost?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Compress files using gzip or snappy.
Option A is correct because compressing JSON files with gzip or Snappy reduces the data size stored in S3, lowering storage costs and minimizing the amount of data that Redshift Spectrum must scan over the network. Redshift Spectrum can read compressed files directly, and compression often improves query performance by reducing I/O, even though it adds a small CPU overhead for decompression.
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.
✓
Compress files using gzip or snappy.
Why this is correct
Compression reduces storage and I/O.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use many small files to maximize parallelism.
Why it's wrong here
Many small files increase overhead; larger files are better.
✓
Partition the data by date (e.g., year/month/day) in S3.
Why this is correct
Partitioning limits the data scanned by queries.
Related concept
Read the scenario before looking for a memorised answer.
✓
Convert JSON files to Apache Parquet format.
Why this is correct
Columnar formats are more efficient for analytics.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create indexes on the S3 data using AWS Glue.
Why it's wrong here
Redshift Spectrum does not use indexes on S3.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that more files equals more parallelism, but in Redshift Spectrum, excessive small files increase overhead and reduce performance, while the correct approach is to use fewer, larger files in a columnar format with partitioning.
Detailed technical explanation
How to think about this question
Redshift Spectrum leverages massively parallel processing by splitting queries into slices that scan S3 objects in parallel; using columnar formats like Parquet with predicate pushdown allows Spectrum to skip entire row groups that don't match WHERE clauses, dramatically reducing data scanned. Partition pruning in Spectrum works by filtering on partition columns (e.g., year, month, day) at the S3 prefix level, so queries that filter on date only scan relevant folders, minimizing data transfer and cost.
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.
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Compress files using gzip or snappy. — Option A is correct because compressing JSON files with gzip or Snappy reduces the data size stored in S3, lowering storage costs and minimizing the amount of data that Redshift Spectrum must scan over the network. Redshift Spectrum can read compressed files directly, and compression often improves query performance by reducing I/O, even though it adds a small CPU overhead for decompression.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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