- A
Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively.
Correct. Tuning autovacuum reduces dead tuple accumulation, minimizing row-level lock contention without application changes.
- B
Increase the RDS instance size to a larger instance class with more vCPUs and memory.
Why wrong: Incorrect. Increasing instance size provides more resources but does not address the root cause of lock contention from dead tuples.
- C
Enable RDS Proxy to manage database connections and reduce connection overhead.
Why wrong: Incorrect. RDS Proxy manages database connections and connection pooling, not lock contention on row-level operations.
- D
Implement table partitioning using the pg_partman extension to split the large table into smaller partitions.
Why wrong: Incorrect. Table partitioning with pg_partman can reduce lock contention by distributing updates across partitions, but it requires changing table schemas and application queries, which is not allowed.
PostgreSQL Autovacuum Tuning: Reducing Lock Contention
This DEA-C01 practice question tests your understanding of data store management. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: autovacuum. 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 runs a transactional database on Amazon RDS for PostgreSQL with Multi-AZ deployment. The database size is 2 TB and experiences moderate write load. The company recently enabled RDS Performance Insights and noticed a high number of 'TupleLock' wait events during peak hours. The development team reports that a batch update job runs every hour, updating millions of rows in a large table. The job takes longer than expected. The DBA suspects that excessive row-level locking is causing contention. The team wants to minimize lock contention without changing the application code. Which solution should be implemented?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively.
The correct answer is A because tuning autovacuum settings (autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) reduces lock contention by cleaning up dead tuples more frequently. In PostgreSQL, row-level locks on heavily updated tables can cause 'TupleLock' wait events. Frequent autovacuum prevents accumulation of dead tuples, reducing the need for lock escalation and shortening update times. Option B (increasing instance size) may improve throughput but does not directly address lock contention. Option C (RDS Proxy) manages connections, not locks. Option D (pg_partman partitioning) reduces row contention but requires application code changes (stem prohibits code changes).
Key principle: Autovacuum
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively.
Why this is correct
Correct. Tuning autovacuum reduces dead tuple accumulation, minimizing row-level lock contention without application changes.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Autovacuum
- ✗
Increase the RDS instance size to a larger instance class with more vCPUs and memory.
Why it's wrong here
Incorrect. Increasing instance size provides more resources but does not address the root cause of lock contention from dead tuples.
- ✗
Enable RDS Proxy to manage database connections and reduce connection overhead.
Why it's wrong here
Incorrect. RDS Proxy manages database connections and connection pooling, not lock contention on row-level operations.
- ✗
Implement table partitioning using the pg_partman extension to split the large table into smaller partitions.
Why it's wrong here
Incorrect. Table partitioning with pg_partman can reduce lock contention by distributing updates across partitions, but it requires changing table schemas and application queries, which is not allowed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume that increasing instance size resolves all performance issues, but lock contention due to dead tuples requires database-level tuning like autovacuum.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Autovacuum
- TupleLock Wait Event
- Dead Tuples
- Multi-AZ Deployment
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
Autovacuum
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 autovacuum, then practise related DEA-C01 questions on the same topic to reinforce the concept.
- →
Data Store Management — study guide chapter
Learn the concepts, then practise the questions
- →
Data Store Management 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 Store Management — This question tests Data Store Management — Autovacuum.
What is the correct answer to this question?
The correct answer is: Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively. — The correct answer is A because tuning autovacuum settings (autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) reduces lock contention by cleaning up dead tuples more frequently. In PostgreSQL, row-level locks on heavily updated tables can cause 'TupleLock' wait events. Frequent autovacuum prevents accumulation of dead tuples, reducing the need for lock escalation and shortening update times. Option B (increasing instance size) may improve throughput but does not directly address lock contention. Option C (RDS Proxy) manages connections, not locks. Option D (pg_partman partitioning) reduces row contention but requires application code changes (stem prohibits code changes).
What should I do if I get this DEA-C01 question wrong?
Review autovacuum, then practise related DEA-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Autovacuum
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 →
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.