The answer is to increase the Flink application’s parallelism, increase the number of shards in the Kinesis data stream, and enable auto-scaling for the Flink application. When a Kinesis Data Analytics Flink application is lagging behind, indicated by a rising MillisBehindLatest metric, and task manager CPU is above 80%, the bottleneck is insufficient processing capacity. Increasing parallelism allows more subtasks to process data concurrently, while adding shards increases the stream’s ingestion partitions, directly reducing the backlog. Enabling auto-scaling ensures the application dynamically adjusts resources as load fluctuates. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Flink’s parallel processing model and Kinesis shard limits—a common trap is to only scale compute without scaling the source stream. Remember the memory tip: “Shards feed the tasks, parallelism chews the backlog, auto-scaling keeps the pace.”
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 data engineer is monitoring an Amazon Kinesis Data Analytics for Apache Flink application that processes streaming data. The application is falling behind (increasing 'MillisBehindLatest') and the CPU utilization of the Flink task managers is consistently above 80%. Which THREE actions should the engineer take to improve performance? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of shards in the Kinesis data stream.
Increasing the number of shards in the Kinesis data stream (Option A) directly increases the ingestion capacity and parallelism source for the Flink application. With more shards, the application can read data from more partitions concurrently, reducing the backlog indicated by 'MillisBehindLatest'. This is a fundamental scaling action for Kinesis-based Flink applications.
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.
✓
Increase the number of shards in the Kinesis data stream.
Why this is correct
More shards allow higher ingestion rate.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the checkpoint interval to reduce state size.
Why it's wrong here
More frequent checkpoints increase overhead.
✓
Enable auto-scaling for the Flink application.
Why this is correct
Auto-scaling adjusts resources based on load.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the number of task managers to reduce CPU contention.
Why it's wrong here
Fewer task managers reduce capacity.
✓
Increase the Flink application's parallelism.
Why this is correct
More parallel tasks improve throughput.
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 decreasing checkpoint intervals with improving performance, not realizing that more frequent checkpoints increase CPU and I/O overhead, making the lag worse.
Detailed technical explanation
How to think about this question
Under the hood, Flink's parallelism determines the number of parallel subtasks for operators, and each subtask can process one Kinesis shard. When 'MillisBehindLatest' grows, it indicates that the consumer (Flink) cannot keep up with the producer (Kinesis). Auto-scaling (Option C) dynamically adjusts the number of task managers based on load, but it requires careful configuration of metrics and scaling policies to avoid thrashing. Increasing parallelism (Option E) distributes the workload across more slots, but must be matched with sufficient shards to avoid idle subtasks.
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 Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of shards in the Kinesis data stream. — Increasing the number of shards in the Kinesis data stream (Option A) directly increases the ingestion capacity and parallelism source for the Flink application. With more shards, the application can read data from more partitions concurrently, reducing the backlog indicated by 'MillisBehindLatest'. This is a fundamental scaling action for Kinesis-based Flink applications.
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
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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 monitoring an Amazon Kinesis Data Analytics application that uses a SQL query to aggregate streaming data. The application is falling behind and the millisBehindLatest metric is increasing. Which action should the engineer take to improve performance?
easy
A.Switch from SQL to Apache Flink for the analytics application
B.Increase the number of shards in the source Kinesis stream
✓ C.Increase the Parallelism setting of the Kinesis Data Analytics application
D.Decrease the window duration of the SQL query
Why C: Increasing the Parallelism setting of the Kinesis Data Analytics application allows the SQL query to process data across more in-application streams and operators concurrently, directly addressing the lag indicated by the rising millisBehindLatest metric. This action scales the compute resources allocated to the application without changing the source stream or the query logic, making it the most direct way to improve throughput for a SQL-based Kinesis Data Analytics application.
Variation 2. A data engineer is monitoring an Amazon Kinesis Data Analytics application that processes real-time clickstream data. The application uses a Flink application with multiple operators. The engineer notices that the 'millisBehindLatest' metric is increasing steadily. Which action is MOST likely to reduce the lag?
hard
A.Decrease the batch size in the Flink application.
B.Switch the source stream to use GZIP compression.
✓ C.Increase the parallelism of the Flink application.
D.Increase the retention period of the Kinesis stream.
Why C: Option D is correct because increasing parallelism can improve throughput and reduce lag. Option A is wrong because increasing the retention period does not affect processing speed. Option B is wrong because decreasing the batch size would reduce throughput. Option C is wrong because using a different compression may reduce storage but not lag.
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Question Discussion
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