MLS-C01 Data Engineering Practice Question
A company runs a real-time fraud detection system using Amazon Kinesis Data Streams with 100 shards. Data is consumed by a custom Java application running on Amazon EC2 instances in an Auto Scaling group. The application processes records and writes results to a DynamoDB table. Over the past month, the application has experienced intermittent slowdowns and the DynamoDB write capacity has been fully utilized during peak hours. The team wants to improve throughput without losing the ability to reprocess failed records. The application currently uses the Kinesis Client Library (KCL) with DynamoDB as the lease table. The team is considering the following changes: A. Increase the number of EC2 instances to match the number of shards. B. Switch to using AWS Lambda as the consumer to handle scaling automatically. C. Increase the write capacity of the DynamoDB lease table to handle more workers. D. Use enhanced fan-out to have each consumer receive its own 2 MB/second shard throughput. Which change should the team implement first to address the issue?
⚠ Common exam trap
Many exam-takers assume increasing DynamoDB write capacity (option A) is the direct fix for write capacity exhaustion, but they miss that enhanced fan-out reduces the underlying cause of those writes by eliminating polling-based contention.
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
✓
Use enhanced fan-out to have each consumer receive its own 2 MB/second shard throughput.
The primary bottleneck is DynamoDB write capacity being fully utilized during peak hours. Enhanced fan-out (option B) provides each consumer with a dedicated 2 MB/second read throughput per shard, eliminating the need for consumers to contend for the shared 2 MB/second per shard. This reduces the load on the DynamoDB lease table because workers no longer need to poll for records, which in turn lowers the write operations to the lease table and alleviates the DynamoDB write capacity issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the write capacity of the DynamoDB lease table to handle more workers.
Why it's wrong here
Lease table capacity is not the bottleneck.
- ✓
Use enhanced fan-out to have each consumer receive its own 2 MB/second shard throughput.
Why this is correct
Enhanced fan-out gives dedicated throughput per consumer.
- ✗
Switch to using AWS Lambda as the consumer to handle scaling automatically.
Why it's wrong here
Lambda does not provide reprocessing capabilities.
- ✗
Increase the number of EC2 instances to match the number of shards.
Why it's wrong here
Adding instances won't increase per-shard throughput.
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
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