- A
Switch to Kinesis Data Analytics for processing.
Why wrong: Incorrect: Data Analytics is for SQL queries, not for improving consumer throughput.
- B
Use enhanced fan-out to dedicate read throughput to the consumer.
Why wrong: Incorrect: Enhanced fan-out is beneficial when multiple consumers read the same stream; for a single consumer, it does not increase throughput.
- C
Increase the record size to 5 KB.
Why wrong: Incorrect: Record size does not affect consumer throughput.
- D
Increase the number of shards in the stream.
Correct: More shards provide more read capacity and allow parallel processing.
Question 178 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 uses Amazon Kinesis Data Streams with a shard count of 5. The data producer sends 1000 records per second, each 1 KB in size. The consumer application reads from the stream using the Kinesis Client Library (KCL) and processes records. The consumer is experiencing high latency and falling behind. What is the most effective way to improve consumer throughput?
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 number of shards in the stream.
The consumer is falling behind because it cannot process records fast enough. The Kinesis Client Library (KCL) typically runs one consumer thread per shard, so with only 5 shards there are only 5 parallel consumers. To improve throughput, the number of shards should be increased to allow more parallel processing. Options like enhanced fan-out would improve read throughput per consumer but do not increase parallelism; the bottleneck here is processing speed, not read throughput. Increasing shards directly increases the number of consumers and thus overall throughput.
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.
- ✗
Switch to Kinesis Data Analytics for processing.
Why it's wrong here
Incorrect: Data Analytics is for SQL queries, not for improving consumer throughput.
- ✗
Use enhanced fan-out to dedicate read throughput to the consumer.
Why it's wrong here
Incorrect: Enhanced fan-out is beneficial when multiple consumers read the same stream; for a single consumer, it does not increase throughput.
- ✗
Increase the record size to 5 KB.
Why it's wrong here
Incorrect: Record size does not affect consumer throughput.
- ✓
Increase the number of shards in the stream.
Why this is correct
Correct: More shards provide more read capacity and allow parallel processing.
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 enhanced fan-out (which provides dedicated throughput per consumer) with solving throughput issues, but fail to realize that with only 5 shards, even dedicated throughput per shard is insufficient for high-volume consumption, making shard scaling the correct solution.
Detailed technical explanation
How to think about this question
Under the hood, Kinesis Data Streams uses a shard-based partitioning model where each shard provides a fixed write capacity of 1 MB/s or 1000 records/s and a read capacity of 2 MB/s (with enhanced fan-out) or 5 transactions/s (shared throughput). The KCL uses a lease-based model where each shard is processed by one consumer worker; if the consumer cannot process records faster than the shard's read limit, latency increases. In practice, scaling out shards also increases the number of parallel consumer workers, enabling horizontal scaling of processing logic.
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.
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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: Increase the number of shards in the stream. — The consumer is falling behind because it cannot process records fast enough. The Kinesis Client Library (KCL) typically runs one consumer thread per shard, so with only 5 shards there are only 5 parallel consumers. To improve throughput, the number of shards should be increased to allow more parallel processing. Options like enhanced fan-out would improve read throughput per consumer but do not increase parallelism; the bottleneck here is processing speed, not read throughput. Increasing shards directly increases the number of consumers and thus overall throughput.
What should I do if I get this MLS-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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Last reviewed: Jul 4, 2026
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