MLS-C01 Data Engineering Practice Question
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?
⚠ Common exam trap
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.
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
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.
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.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.