Quick Answer
The answer is that defining sort keys for commonly filtered columns is one of the three critical design considerations for performance optimization when migrating a large Oracle data warehouse to Amazon Redshift. This is correct because sort keys physically order data on disk within each node, allowing Redshift’s zone maps to skip entire blocks of data that don’t match query filters, dramatically reducing the amount of data scanned. Alongside sort keys, you must also choose an appropriate distribution style—such as KEY, EVEN, or ALL—to minimize data shuffling during joins, and apply column compression encodings to reduce storage and I/O. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding that Redshift is a columnar, massively parallel processing (MPP) database, so performance hinges on data locality and scan efficiency. A common trap is focusing only on distribution styles while neglecting sort keys and compression, which together form the three pillars of Redshift optimization. Memory tip: think “Sort, Distribute, Compress” as the three levers you pull to make Redshift fast.
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 Amazon Redshift. Which THREE design considerations are important for performance optimization?
Choose appropriate distribution styles (KEY, ALL, EVEN).
Affects data distribution across nodes.
Use compression encodings for columns.
Reduces storage and improves I/O.
Define sort keys for commonly filtered columns.
Improves query performance.
Enable cross-Region replication for data locality.
Why wrong: Not a performance optimization.
Use row-based storage for fact tables.
Why wrong: Redshift uses columnar storage.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Choose appropriate distribution styles (KEY, ALL, EVEN).
Option A is correct because distribution styles (KEY, ALL, EVEN) control how data is distributed across compute nodes in Amazon Redshift. Choosing the right distribution style minimizes data movement during joins and aggregations, which is critical for performance in a large data warehouse migration from Oracle. For example, using KEY distribution on a frequently joined column keeps related rows on the same node, reducing network traffic.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Choose appropriate distribution styles (KEY, ALL, EVEN).
Why this is correct
Affects data distribution across nodes.
Related concept
Read the scenario before looking for a memorised answer.
Use compression encodings for columns.
Why this is correct
Reduces storage and improves I/O.
Related concept
Read the scenario before looking for a memorised answer.
Define sort keys for commonly filtered columns.
Why this is correct
Improves query performance.
Related concept
Read the scenario before looking for a memorised answer.
Enable cross-Region replication for data locality.
Why it's wrong here
Not a performance optimization.
Use row-based storage for fact tables.
Why it's wrong here
Redshift uses columnar storage.
Common exam traps
The trap here is that candidates may confuse disaster recovery features (like cross-Region replication) with performance design choices, or mistakenly think row-based storage applies to Redshift because of their Oracle background, where row storage is the norm.
Detailed technical explanation
Under the hood, Redshift distributes data slices across nodes based on the distribution style, and each node processes its slice in parallel. Sort keys physically order data on disk, enabling Redshift to use zone maps to skip irrelevant blocks during scans, drastically reducing I/O for range-restricted queries. Compression encodings like AZ64 or ZSTD are applied per column and are automatically recommended by the COPY command or ANALYZE COMPRESSION, reducing storage footprint and memory usage.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
The correct answer is: Choose appropriate distribution styles (KEY, ALL, EVEN). — Option A is correct because distribution styles (KEY, ALL, EVEN) control how data is distributed across compute nodes in Amazon Redshift. Choosing the right distribution style minimizes data movement during joins and aggregations, which is critical for performance in a large data warehouse migration from Oracle. For example, using KEY distribution on a frequently joined column keeps related rows on the same node, reducing network traffic.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
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Same concept, more angles
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 uses Amazon Redshift for data warehousing. They run a query that joins a large fact table (10 billion rows) with a small dimension table (1 million rows). The query is slow. The distribution style of the fact table is AUTO, and the dimension table has DISTSTYLE ALL. The join key is user_id. What is the MOST likely reason for the poor performance?
mediumWhy B: Redshift distributes data across slices. For joins, distribution keys should align to avoid data redistribution. With DISTSTYLE ALL on the dimension table, it is copied to all nodes, which is good. The fact table uses AUTO, which may choose a suboptimal distribution key. If the fact table is distributed by user_id, it should be fine. However, the issue may be that the dimension table is not sorted on the join key, or the fact table's distribution key is not user_id. Option B is wrong because DISTSTYLE ALL is actually beneficial for small tables. Option C is wrong because no sort key on dimension table is less critical. Option D is wrong because column compression helps scan performance but not joins.
Last reviewed: Jun 24, 2026
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