- A
Disable checkpointing to avoid failures.
Why wrong: Disabling checkpointing removes fault tolerance.
- B
Switch the state backend from in-memory to RocksDB.
Why wrong: RocksDB helps manage large state but does not directly fix checkpoint failures.
- C
Increase the parallelism of the application.
Why wrong: Higher parallelism can increase checkpoint overhead.
- D
Increase the checkpointing interval.
Longer intervals reduce checkpoint frequency and associated failures.
Quick Answer
The answer is to increase the checkpointing interval. This is the correct configuration change because when an Apache Flink application manages a large state, the checkpoint operation becomes I/O and CPU intensive, and setting the interval too tight creates backpressure and resource contention as the system struggles to complete one checkpoint before the next begins. By lengthening the interval, you give the state backend sufficient time to persist the large state snapshot reliably, reducing the likelihood of failures that cause data reprocessing. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Flink checkpoint reliability trade-offs—a common trap is to assume that more frequent checkpoints improve reliability, but with large state, the opposite is true. Remember the mnemonic: "Large state, longer wait—tight intervals seal your fate."
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that the application's checkpointing is failing intermittently, causing data reprocessing. The application uses a large state. Which configuration change should the team make to improve checkpoint reliability?
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
Increase the checkpointing interval.
Increasing the checkpointing interval reduces the frequency of checkpoint operations, giving the system more time to complete each checkpoint before the next one starts. This alleviates backpressure and resource contention, which is critical when dealing with large state, as checkpointing large state is I/O and CPU intensive and can fail if intervals are too tight.
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.
- ✗
Disable checkpointing to avoid failures.
Why it's wrong here
Disabling checkpointing removes fault tolerance.
- ✗
Switch the state backend from in-memory to RocksDB.
Why it's wrong here
RocksDB helps manage large state but does not directly fix checkpoint failures.
- ✗
Increase the parallelism of the application.
Why it's wrong here
Higher parallelism can increase checkpoint overhead.
- ✓
Increase the checkpointing interval.
Why this is correct
Longer intervals reduce checkpoint frequency and associated failures.
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 often confuse improving state backend performance (RocksDB) with fixing checkpoint reliability, when the root cause is checkpoint timing pressure, not state storage efficiency.
Detailed technical explanation
How to think about this question
Under the hood, Flink's checkpointing uses a synchronous barrier mechanism; if a checkpoint takes longer than the configured interval, the next checkpoint is triggered immediately, creating a backlog that can overwhelm the JobManager and cause failures. In large-state applications, the checkpoint duration is dominated by the time to snapshot state to durable storage (e.g., S3), so increasing the interval provides a necessary buffer. A real-world scenario is a streaming pipeline with multi-GB state where a 1-minute interval causes cascading failures, but a 5-minute interval stabilizes the system.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
- →
Data Ingestion and Transformation — study guide chapter
Learn the concepts, then practise the questions
- →
Data Ingestion and Transformation 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 Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the checkpointing interval. — Increasing the checkpointing interval reduces the frequency of checkpoint operations, giving the system more time to complete each checkpoint before the next one starts. This alleviates backpressure and resource contention, which is critical when dealing with large state, as checkpointing large state is I/O and CPU intensive and can fail if intervals are too tight.
What should I do if I get this DEA-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 →
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 company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and write…
- A data engineer is designing a serverless data ingestion pipeline that uses Amazon Kinesis Data Firehose to deliver data…
- A company runs a nightly AWS Glue ETL job that reads from a JDBC source (PostgreSQL) and writes to S3 in Parquet format.…
- Match each AWS database service to its primary use case.
Last reviewed: Jun 11, 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.