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Question 106 of 1,786

Data Ingestion and Transformation →mediumMultiple ChoiceObjective-mapped

Flink Parallelism and Shard Matching for Latency Reduction

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 is using Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an Amazon S3 bucket. The team notices that the application is experiencing high latency during peak hours. The stream has 8 shards, and the application is configured with a parallelism of 4. Which action would most likely reduce the latency?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

A
Decrease the batch size in the S3 sink.
Why wrong: Batch size affects file size, not processing latency; Flink processes records continuously.
B
Use a larger Kinesis Data Analytics application instance type.
Why wrong: Larger instance provides more memory/CPU but may not solve parallelism mismatch; also more costly.
C
Increase the parallelism of the Flink application to 8.
Matching parallelism to shard count ensures each shard is processed concurrently, reducing backpressure.
D
Increase the checkpointing interval to reduce overhead.
Why wrong: Checkpointing interval affects fault tolerance, not throughput; increasing it may reduce latency slightly but not address the core issue.

Full breakdown with real-world context →

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 parallelism of the Flink application to 8.

The application has 8 shards but only a parallelism of 4, meaning each Flink subtask must process data from 2 shards. This creates a bottleneck because a single subtask cannot process data from multiple shards faster than the slowest shard's throughput. Increasing parallelism to 8 matches the shard count, allowing each subtask to read from exactly one shard, eliminating the contention and reducing latency.

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.

✗
Decrease the batch size in the S3 sink.
Why it's wrong here
Batch size affects file size, not processing latency; Flink processes records continuously.
✗
Use a larger Kinesis Data Analytics application instance type.
Why it's wrong here
Larger instance provides more memory/CPU but may not solve parallelism mismatch; also more costly.
✓
Increase the parallelism of the Flink application to 8.
Why this is correct
Matching parallelism to shard count ensures each shard is processed concurrently, reducing backpressure.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the checkpointing interval to reduce overhead.
Why it's wrong here
Checkpointing interval affects fault tolerance, not throughput; increasing it may reduce latency slightly but not address the core issue.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The DEA-C01 exam often tests the misconception that increasing instance size (Option B) or tuning sink parameters (Option A) will fix latency, when the root cause is a parallelism-to-shard mismatch that only increasing parallelism can resolve.

Detailed technical explanation

How to think about this question

In Kinesis Data Analytics for Apache Flink, the Kinesis Data Streams connector uses shard discovery and assigns each shard to a Flink subtask. When parallelism is less than the number of shards, the connector uses a round-robin or hash-based assignment, causing some subtasks to handle multiple shards. This violates the Flink model of one subtask per shard for optimal throughput, as each subtask must poll multiple shards sequentially, increasing per-record latency. Real-world scenarios with uneven shard traffic (hot shards) exacerbate this issue, making parallelism equal to shard count critical for low-latency streaming.

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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

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.

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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 parallelism of the Flink application to 8. — The application has 8 shards but only a parallelism of 4, meaning each Flink subtask must process data from 2 shards. This creates a bottleneck because a single subtask cannot process data from multiple shards faster than the slowest shard's throughput. Increasing parallelism to 8 matches the shard count, allowing each subtask to read from exactly one shard, eliminating the contention and reducing latency.

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.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

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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Same concept, more angles

1 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 company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application reads from a Kinesis stream and writes aggregated results to an Amazon S3 bucket. The company notices that the application is falling behind and the checkpoint duration is increasing. Which THREE actions should the data engineer take to improve performance? (Choose THREE.)

hard

A.Decrease the number of shards in the source Kinesis stream.
✓ B.Use multiple S3 prefixes in the output path to avoid throttling.
C.Increase the heap memory of the Flink application.
✓ D.Increase the checkpoint interval to reduce checkpoint overhead.
✓ E.Increase the parallelism of the Flink application.

Why B: Options B, D, and E are correct. Using multiple S3 prefixes in the output path (B) reduces the risk of S3 write throttling by distributing writes across multiple partition keys. Increasing the checkpoint interval (D) reduces the frequency of checkpointing, thus decreasing the overhead and allowing the application to process more data between checkpoints. Increasing parallelism (E) allows the Flink application to process more data in parallel, improving throughput. Decreasing the number of shards (A) would reduce the incoming data rate and potentially worsen the lag. Increasing heap memory (C) might help with memory pressure but does not directly address checkpoint duration or processing lag; the primary issues are related to parallelism and checkpoint overhead.

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