The answer is to increase the parallelism of the Kinesis Data Analytics application to match the number of shards. This is correct because Kinesis Data Analytics processes data from each shard using parallel operators, and with a parallelism of only 1, the single processing thread cannot handle the doubled data volume across 10 shards, causing the iterator age to grow. By setting parallelism to 10, the application reads from all shards concurrently, distributing the load and reducing lag without data loss or duplicates, as KDA manages exactly-once semantics internally. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how KDA parallelism directly maps to shard count for scaling throughput, and a common trap is to increase shards instead of application parallelism. Remember the memory tip: “One shard, one thread—match parallelism to shard count to stay ahead.”
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 company runs an e-commerce platform that generates clickstream data from millions of users. The data is ingested into Amazon Kinesis Data Streams with a shard count of 10. The data is then consumed by a Kinesis Data Analytics application that runs SQL queries to aggregate metrics in real time. Recently, the application has been falling behind, and the stream's iterator age metric is increasing. The data volume has doubled over the past month. The application currently uses a single Kinesis Data Analytics application with parallelism of 1. Which action should the data engineer take to improve the processing rate and reduce the iterator age without losing data or causing duplicates?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the parallelism of the Kinesis Data Analytics application to match the number of shards.
Option D is correct because Kinesis Data Analytics (KDA) processes data from each shard in a stream using one or more parallel operators. With a parallelism of 1, the application uses only a single processing thread, which cannot keep up with the doubled data volume across 10 shards. By increasing parallelism to match the shard count (10), KDA can read from all shards concurrently, distributing the processing load and reducing the iterator age without data loss or duplicates, as KDA manages checkpointing and exactly-once semantics internally.
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.
✗
Change the Kinesis Data Analytics application to use a Kinesis Data Firehose delivery stream as the source.
Why it's wrong here
Firehose is not a real-time analytics solution; it delivers data to destinations, not for streaming SQL.
✗
Reduce the retention period of the Kinesis Data Streams to 24 hours.
Why it's wrong here
Reducing retention does not speed up processing; it may cause data loss.
✗
Increase the number of shards in the Kinesis Data Streams to 20.
Why it's wrong here
More shards without increasing parallelism will not help; the application still processes one shard at a time.
✓
Increase the parallelism of the Kinesis Data Analytics application to match the number of shards.
Why this is correct
Higher parallelism allows concurrent processing of multiple shards.
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 assume increasing shard count (Option C) is the only way to handle higher data volume, but they overlook that the processing application's parallelism must also scale to consume the additional shards, otherwise the bottleneck shifts to the consumer.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics uses a 'parallelism per shard' model where each processing unit (operator) can read from one or more shards, but with parallelism set to 1, the application is limited to a single KCL worker that polls all shards sequentially, creating a severe bottleneck. Under the hood, KDA leverages the Kinesis Client Library (KCL) with DynamoDB-backed checkpointing; increasing parallelism allows multiple workers to lease shards independently, enabling horizontal scaling that matches the stream's shard count. In real-world scenarios, failing to align parallelism with shard count is a common cause of lag, especially when data volume grows unpredictably.
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 — 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 parallelism of the Kinesis Data Analytics application to match the number of shards. — Option D is correct because Kinesis Data Analytics (KDA) processes data from each shard in a stream using one or more parallel operators. With a parallelism of 1, the application uses only a single processing thread, which cannot keep up with the doubled data volume across 10 shards. By increasing parallelism to match the shard count (10), KDA can read from all shards concurrently, distributing the processing load and reducing the iterator age without data loss or duplicates, as KDA manages checkpointing and exactly-once semantics internally.
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.
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Question Discussion
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