The answer is to use Amazon Redshift as the database engine, with distribution keys based on frequently joined columns and sort keys aligned with query filter predicates. This trio of design decisions is critical because Redshift is a columnar, massively parallel processing (MPP) data warehouse that distributes data across compute nodes; choosing the right distribution style—specifically KEY distribution on join columns—co-locates related rows on the same slice, drastically reducing the network shuffle required for complex analytical queries on 50 TB of data. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of Redshift’s architecture under concurrency and performance constraints, with a common trap being to select automatic distribution or EVEN distribution, which can cause excessive data movement for large joins. Remember the mnemonic “Join on KEY, filter with SORT” to anchor the core design principle for high-performance warehouse migrations.
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 migrating a large Oracle data warehouse to AWS. The warehouse contains 50 TB of data and runs complex analytical queries. The solution must support concurrency of up to 100 users and provide high performance for queries. Which THREE design decisions should the company make? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use distribution keys based on frequently joined columns
Distribution keys based on frequently joined columns ensure that related data is co-located on the same compute nodes, minimizing data movement across the network during joins. This is critical for complex analytical queries on large datasets in Amazon Redshift, as it reduces shuffle overhead and improves query performance.
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 distribution keys based on frequently joined columns
Why this is correct
Distribution keys enable parallel processing and reduce data movement.
Related concept
Read the scenario before looking for a memorised answer.
✓
Design tables with columnar storage
Why this is correct
Columnar storage is efficient for analytical queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon RDS for Oracle with Multi-AZ
Why it's wrong here
RDS is for OLTP, not analytical workloads.
✗
Use Amazon DynamoDB with global tables
Why it's wrong here
DynamoDB is NoSQL and not suitable for complex analytical queries.
✓
Use Amazon Redshift as the database engine
Why this is correct
Redshift is purpose-built for data warehousing and analytics.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Amazon RDS for Oracle (an OLTP database) with a suitable data warehouse solution, overlooking that Redshift’s columnar storage and MPP architecture are specifically designed for large-scale analytical workloads.
Detailed technical explanation
How to think about this question
Amazon Redshift uses a massively parallel processing (MPP) architecture where data is distributed across multiple compute nodes. Distribution keys control how rows are distributed; choosing a key based on frequently joined columns enables collocated joins, avoiding the expensive redistribution of data across the network. Columnar storage in Redshift stores data by column rather than by row, allowing it to read only the columns needed for a query, drastically reducing I/O and improving compression ratios—especially beneficial for analytical queries that scan large subsets of columns.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Use distribution keys based on frequently joined columns — Distribution keys based on frequently joined columns ensure that related data is co-located on the same compute nodes, minimizing data movement across the network during joins. This is critical for complex analytical queries on large datasets in Amazon Redshift, as it reduces shuffle overhead and improves query performance.
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.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is migrating an on-premises Oracle data warehouse to AWS. The warehouse contains 20 TB of data and supports complex SQL queries with joins and aggregations. The migration should minimize downtime and require minimal changes to existing SQL queries. Which database service is MOST appropriate?
hard
A.Amazon RDS for Oracle
B.Amazon DynamoDB
C.Amazon ElastiCache for Redis
✓ D.Amazon Redshift
Why D: Amazon Redshift is the most appropriate choice because it is a fully managed, petabyte-scale data warehouse service designed for complex SQL queries with joins and aggregations. It supports standard SQL with minimal changes to existing queries, and its columnar storage and massively parallel processing (MPP) architecture are optimized for analytical workloads. The 20 TB data size and requirement to minimize downtime align with Redshift's ability to perform online resizing and use features like RA3 nodes with managed storage for elastic scaling.
Variation 2. A company is migrating an on-premises Oracle data warehouse to AWS. The warehouse contains 50 TB of data and runs complex queries that involve joins and aggregations. The team wants to minimize migration effort and cost while maintaining query performance. Which AWS service should they use?
medium
A.Amazon RDS for Oracle
B.Amazon ElastiCache for Redis
✓ C.Amazon Redshift
D.Amazon DynamoDB
Why C: Option C is correct because Amazon Redshift is a fully managed petabyte-scale data warehouse optimized for complex queries. Option A is wrong because RDS is for OLTP, not OLAP. Option B is wrong because ElastiCache is in-memory caching, not a data warehouse. Option D is wrong because DynamoDB is NoSQL and not suited for complex joins.
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This DBS-C01 practice question is part of Courseiva's free Amazon Web Services 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 DBS-C01 exam.
Question Discussion
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