- A
Set DISTSTYLE to ALL for both tables.
Why wrong: ALL distributes entire table to each node, increasing broadcast cost.
- B
Change the distribution style of large tables to KEY on the join column.
KEY distribution collocates data on the same slice, reducing redistribution.
- C
Increase the number of slices by resizing the cluster.
More slices reduce the amount of data per slice and improve parallelism.
- D
Define SORTKEYs on the join columns.
Why wrong: SORTKEYs help with range scans, not joins.
- E
Drop and recreate the tables with the same DDL.
Why wrong: Recreating tables with same DDL does not change performance.
Question 30 of 1,786
How to Reduce DS_DIST_ALL_INNER and DS_BCAST_INNER Operations in Redshift
This DEA-C01 practice question tests your understanding of data operations and support. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data engineer is troubleshooting a slow Amazon Redshift query. The query plan shows a large number of 'DS_DIST_ALL_INNER' and 'DS_BCAST_INNER' operations. Which TWO actions would likely improve query performance?
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
✓
Change the distribution style of large tables to KEY on the join column.
Option B is correct because using DISTSTYLE KEY on the join column for large tables colocates data with the same key on the same slice, reducing the need for data redistribution operations like DS_DIST_ALL_INNER and DS_BCAST_INNER. Option C is correct because increasing the number of slices by resizing the cluster distributes data across more compute nodes, allowing more parallelism and reducing the relative impact of data redistribution. Option A is incorrect because setting DISTSTYLE to ALL on both tables would broadcast each table to all nodes, increasing data movement and likely worsening performance. Option D is incorrect because SORTKEYs optimize range-restricted scans and sorting, not join data movement. Option E is incorrect because dropping and recreating tables with the same DDL does not change distribution or sort strategies, so it does not address the root cause of excessive redistribution.
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.
- ✗
Set DISTSTYLE to ALL for both tables.
Why it's wrong here
ALL distributes entire table to each node, increasing broadcast cost.
- ✓
Change the distribution style of large tables to KEY on the join column.
Why this is correct
KEY distribution collocates data on the same slice, reducing redistribution.
Related concept
CIDR notation defines the prefix length.
- ✓
Increase the number of slices by resizing the cluster.
Why this is correct
More slices reduce the amount of data per slice and improve parallelism.
Related concept
CIDR notation defines the prefix length.
- ✗
Define SORTKEYs on the join columns.
Why it's wrong here
SORTKEYs help with range scans, not joins.
- ✗
Drop and recreate the tables with the same DDL.
Why it's wrong here
Recreating tables with same DDL does not change performance.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 DEA-C01 subnetting questions on CIDR, address ranges, and subnet selection.
- →
Data Operations and Support — study guide chapter
Learn the concepts, then practise the questions
- →
Data Operations and Support practice questions
Targeted practice on this topic area only
- →
All DEA-C01 questions
1,786 questions across all exam domains
- →
AWS Certified Data Engineer Associate DEA-C01 study guide
Full concept coverage aligned to exam objectives
- →
DEA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DEA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Ingestion and Transformation practice questions
Practise DEA-C01 questions linked to Data Ingestion and Transformation.
Data Operations and Support practice questions
Practise DEA-C01 questions linked to Data Operations and Support.
Data Security and Governance practice questions
Practise DEA-C01 questions linked to Data Security and Governance.
Data Store Management practice questions
Practise DEA-C01 questions linked to Data Store Management.
DEA-C01 fundamentals practice questions
Practise DEA-C01 questions linked to DEA-C01 fundamentals.
DEA-C01 scenario practice questions
Practise DEA-C01 questions linked to DEA-C01 scenario.
DEA-C01 troubleshooting practice questions
Practise DEA-C01 questions linked to DEA-C01 troubleshooting.
Practice this exam
Start a free DEA-C01 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 DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Change the distribution style of large tables to KEY on the join column. — Option B is correct because using DISTSTYLE KEY on the join column for large tables colocates data with the same key on the same slice, reducing the need for data redistribution operations like DS_DIST_ALL_INNER and DS_BCAST_INNER. Option C is correct because increasing the number of slices by resizing the cluster distributes data across more compute nodes, allowing more parallelism and reducing the relative impact of data redistribution. Option A is incorrect because setting DISTSTYLE to ALL on both tables would broadcast each table to all nodes, increasing data movement and likely worsening performance. Option D is incorrect because SORTKEYs optimize range-restricted scans and sorting, not join data movement. Option E is incorrect because dropping and recreating tables with the same DDL does not change distribution or sort strategies, so it does not address the root cause of excessive redistribution.
What should I do if I get this DEA-C01 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 DEA-C01 subnetting questions on CIDR, address ranges, and subnet selection.
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 →
Same concept, more angles
2 more ways this is tested on DEA-C01
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 data engineer is troubleshooting a slow-running Amazon Redshift query. The query joins several large tables and performs aggregations. The engineer runs EXPLAIN and sees a 'DS_DIST_ALL' step. Which TWO actions will MOST likely improve query performance? (Choose TWO.)
hard- A.Run the VACUUM command on all tables.
- B.Use the CNAME command to rename the tables.
- ✓ C.Change the distribution style of the tables to DISTSTYLE KEY on the join columns.
- D.Increase the number of nodes in the Redshift cluster.
- ✓ E.Define appropriate SORTKEYs on the tables based on the query predicates.
Why C: The DS_DIST_ALL step in the query plan indicates that data is being broadcast from one node to all others, causing significant network overhead. Option C is correct because changing the distribution style to DISTSTYLE KEY on the join columns ensures that matching rows are co-located on the same node, reducing the need for redistribution. Option E is correct because defining appropriate SORTKEYs based on query predicates allows the query optimizer to use zone maps to skip irrelevant blocks, speeding up scans and aggregations. Option A is incorrect; VACUUM reorganizes data on disk but does not affect distribution. Option B is incorrect; CNAME is a DNS record type, not a Redshift command. Option D is incorrect; increasing nodes might not directly fix the distribution issue and is less targeted than changing distribution style.
Variation 2. A data engineer is troubleshooting a slow-running Amazon Redshift query. The query involves a large fact table with a distribution style of EVEN and a sort key on date. The table has 10 slices. The engineer notices that the query is performing a broadcast join with a small dimension table. Which change would most improve performance?
hard- A.Remove the sort key and use a compound sort key on the join column
- B.Change the dimension table to DISTSTYLE ALL
- C.Increase the number of slices by resizing the cluster
- ✓ D.Change the fact table to DISTSTYLE KEY on the join column
Why D: Option D is correct because changing the fact table's distribution style to KEY on the join column co-locates the rows from both tables on the same compute nodes, significantly reducing the need for data movement during the join. A broadcast join moves the entire small dimension table to every slice, which is inefficient; KEY distribution on the fact table aligns the join keys across nodes, enabling a distributed join without broadcasting. Option A is wrong: removing the sort key would hurt range-based filtering performance and the suggestion of a compound sort key on the join column does not address data distribution. Option B is wrong: although ALL distribution on the small dimension table is a good practice, the question asks for a change that most improves performance for this specific scenario; it is less effective than fixing the fact table distribution. Option C is wrong: increasing slices by resizing the cluster does not change the distribution or join strategy; it only adds more parallelism but still requires broadcasting.
Keep practising
More DEA-C01 practice questions
- A data pipeline uses Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data volume spikes occasionall…
- An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parque…
- A data engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that…
- A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and write…
- A data engineer applies the above bucket policy to an S3 bucket containing sensitive data. The goal is to allow only enc…
- A company uses AWS Glue to catalog data in Amazon S3. The security team requires that all sensitive data be identified a…
Last reviewed: Jun 20, 2026
This DEA-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 DEA-C01 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.