MLS-C01 Data Engineering Practice Question
A company uses Kinesis Data Streams to ingest real-time sensor data. The data is consumed by a Lambda function that writes to DynamoDB. During peak hours, the Lambda function throws ProvisionedThroughputExceededException. The team wants to decouple the write operation and improve resilience. What should they do?
⚠ Common exam trap
Watch out — candidates often assume adding a queue (SQS) is the standard decoupling pattern, but in this context, Kinesis Firehose is purpose-built for stream ingestion with built-in error handling and Lambda integration, making it a more direct and efficient solution than introducing an additional queue layer.
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 Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.
Kinesis Firehose can consume data from a Kinesis Data Stream and invoke a Lambda function for transformation before delivering to destinations like DynamoDB. By using Firehose with error handling, the team decouples the write operation from the Lambda consumer, allowing Firehose to buffer data and retry failed writes, which improves resilience against ProvisionedThroughputExceededException without losing data.
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 Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.
Why this is correct
Firehose buffers data, retries on failures, and decouples the producer from DynamoDB writes.
- ✗
Increase the Lambda function's reserved concurrency and provision more DynamoDB write capacity.
Why it's wrong here
Increasing capacity may help temporarily but does not decouple the system; throttling can still occur.
- ✗
Place the Lambda function's output into an Amazon SQS queue, and have a second Lambda function write to DynamoDB.
Why it's wrong here
SQS is not directly integrated with Kinesis; additional components increase complexity.
- ✗
Use Kinesis Data Analytics to process the stream and write results directly to DynamoDB.
Why it's wrong here
Kinesis Data Analytics does not have a built-in DynamoDB sink; it can output to Firehose or Lambda.
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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