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Question 222 of 1,755

Data Engineering →hardMultiple SelectObjective-mapped

Quick Answer

The answer is enhanced fan-out for consumers and auto-scaling shards. Enhanced fan-out provides each consumer with a dedicated 2 MB/second read throughput per shard, eliminating the contention that occurs with standard consumers when multiple applications poll the same stream, which directly achieves the sub-second latency required for the 10,000 events per second pipeline. Auto-scaling shards dynamically adjusts the number of shards based on the incoming write throughput, ensuring the stream never becomes throttled during traffic spikes without the cost of over-provisioning. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of Kinesis Data Streams’ low latency capabilities for real-time ML inference pipelines, where a common trap is choosing to increase shards to 100—this solves capacity but wastes cost and ignores the dedicated throughput benefit of enhanced fan-out. Remember the memory tip: “Fan-out for fast reads, auto-scale for writes.”

MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is designing a streaming data pipeline that ingests 10,000 events per second. Each event is 2 KB. The pipeline must process events with a latency of less than 1 second. The team is considering using Amazon Kinesis Data Streams with 10 shards. Which TWO additional configurations should the team implement to meet the latency requirement? (Choose two.)

Question 1hardmulti select

Full question →

A
Use record aggregation to reduce the number of records.
Why wrong: Record aggregation reduces the number of PUT requests but does not reduce read latency.
B
Configure Kinesis Data Firehose to deliver data to Amazon S3.
Why wrong: Firehose delivery is for batch loading, not for low-latency processing.
C
Increase the number of shards to 100.
Why wrong: More shards increase cost and may not be necessary; 10 shards provide 10 MB/s write capacity, sufficient for 20 MB/s (10K events * 2 KB).
D
Enable auto-scaling of shards based on throughput.
Auto-scaling ensures that the stream has enough shards to handle peak load without manual intervention.
E
Use enhanced fan-out for consumers.
Enhanced fan-out provides dedicated read throughput to each consumer, reducing latency.

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

✓

Enable auto-scaling of shards based on throughput.

Correct options: B and D. Using enhanced fan-out allows multiple consumers to read from the stream with dedicated throughput, reducing latency. Auto-scaling shards ensures sufficient capacity as load varies. Option A (increase shards to 100) is over-provisioning and increases cost. Option C (record aggregation) is for reducing PUT costs but doesn't affect read latency. Option E (S3 delivery) is not relevant to 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.

✗
Use record aggregation to reduce the number of records.
Why it's wrong here
Record aggregation reduces the number of PUT requests but does not reduce read latency.
✗
Configure Kinesis Data Firehose to deliver data to Amazon S3.
Why it's wrong here
Firehose delivery is for batch loading, not for low-latency processing.
✗
Increase the number of shards to 100.
Why it's wrong here
More shards increase cost and may not be necessary; 10 shards provide 10 MB/s write capacity, sufficient for 20 MB/s (10K events * 2 KB).
✓
Enable auto-scaling of shards based on throughput.
Why this is correct
Auto-scaling ensures that the stream has enough shards to handle peak load without manual intervention.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use enhanced fan-out for consumers.
Why this is correct
Enhanced fan-out provides dedicated read throughput to each consumer, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Enable auto-scaling of shards based on throughput. — Correct options: B and D. Using enhanced fan-out allows multiple consumers to read from the stream with dedicated throughput, reducing latency. Auto-scaling shards ensures sufficient capacity as load varies. Option A (increase shards to 100) is over-provisioning and increases cost. Option C (record aggregation) is for reducing PUT costs but doesn't affect read latency. Option E (S3 delivery) is not relevant to latency.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-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 MLS-C01 exam.