MLS-C01 Data Engineering Practice Question
A data pipeline uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that transforms and writes to Amazon DynamoDB. The Lambda function is throttled during traffic spikes, causing data to be reprocessed. Which solution should the team implement to handle the throttling without losing data?
⚠ Common exam trap
Test-takers frequently confuse Lambda throttling with DynamoDB write capacity issues, leading them to choose DynamoDB auto scaling (Option C) instead of addressing the upstream Kinesis shard count and Lambda error handling with a DLQ.
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 and configure a dead-letter queue (DLQ) for the Lambda function.
Increasing the number of shards in the Kinesis stream raises the throughput capacity, reducing the likelihood of Lambda throttling. Configuring a dead-letter queue (DLQ) for the Lambda function captures any records that fail processing after exhausting retries, preventing data loss. This combination addresses both the throttling cause and provides a safety net for unprocessed records.
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 Amazon SQS as an intermediate buffer between Kinesis and Lambda.
Why it's wrong here
Adding SQS introduces unnecessary complexity; Kinesis already provides buffering.
- ✓
Increase the number of shards in the Kinesis stream and configure a dead-letter queue (DLQ) for the Lambda function.
Why this is correct
More shards increase parallelism; DLQ captures failures for reprocessing.
- ✗
Enable DynamoDB auto scaling to handle writes.
Why it's wrong here
The bottleneck is Lambda throttling, not DynamoDB capacity.
- ✗
Reduce the batch size in the Lambda event source mapping.
Why it's wrong here
Smaller batches may reduce throttling but increase number of invocations and cost.
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.