- A
Send the data to Amazon SQS first and then process with Lambda
Why wrong: Adding SQS introduces latency and does not address the core stream capacity issue.
- B
Use Amazon Kinesis Data Firehose instead of Kinesis Data Streams
Why wrong: Firehose is for delivery to destinations, not for real-time processing with Lambda.
- C
Increase the Lambda function's batch size and reduce the batch window
Why wrong: Larger batch sizes can cause timeouts; reducing batch window increases invocation frequency.
- D
Increase the number of shards in the Kinesis stream
More shards increase parallelism and throughput, reducing throttling.
Question 618 of 1,755
Increase Kinesis Data Streams Throughput by Adding Shards
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 streaming data from thousands of devices using Amazon Kinesis Data Streams. The data is consumed by a AWS Lambda function that processes each record. The Lambda function is experiencing high error rates and throttling due to the volume of data. Which action would MOST effectively improve the processing throughput and reduce errors?
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 Kinesis stream
Option D is correct because increasing the number of shards in the Kinesis stream directly increases the stream's capacity for data ingestion and processing parallelism. Each shard supports up to 1 MB/s or 1,000 records/s for writes, and Lambda processes records from each shard concurrently. By adding more shards, you distribute the load across more Lambda invocations, reducing throttling and error rates caused by exceeding the per-shard throughput limits.
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.
- ✗
Send the data to Amazon SQS first and then process with Lambda
Why it's wrong here
Adding SQS introduces latency and does not address the core stream capacity issue.
- ✗
Use Amazon Kinesis Data Firehose instead of Kinesis Data Streams
Why it's wrong here
Firehose is for delivery to destinations, not for real-time processing with Lambda.
- ✗
Increase the Lambda function's batch size and reduce the batch window
Why it's wrong here
Larger batch sizes can cause timeouts; reducing batch window increases invocation frequency.
- ✓
Increase the number of shards in the Kinesis stream
Why this is correct
More shards increase parallelism and throughput, reducing throttling.
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 Kinesis Data Streams with Kinesis Data Firehose, thinking Firehose can handle high-volume Lambda processing, when in fact Firehose is a delivery service with no per-record Lambda integration.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the unit of parallelism; each shard is processed by one Lambda invocation at a time, and the number of concurrent Lambda executions equals the number of shards. When throttling occurs, it is often because the total write throughput exceeds the sum of shard limits (e.g., 5 shards = 5 MB/s write capacity). Increasing shards also increases the number of Lambda concurrency slots, allowing more records to be processed in parallel without backpressure. In practice, you must also ensure the Lambda function's reserved concurrency is set high enough to avoid throttling at the Lambda service level.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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 Kinesis stream — Option D is correct because increasing the number of shards in the Kinesis stream directly increases the stream's capacity for data ingestion and processing parallelism. Each shard supports up to 1 MB/s or 1,000 records/s for writes, and Lambda processes records from each shard concurrently. By adding more shards, you distribute the load across more Lambda invocations, reducing throttling and error rates caused by exceeding the per-shard throughput limits.
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
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.
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