MLS-C01 Data Engineering Practice Question
A data engineering team is designing a data pipeline that processes streaming data from Amazon Kinesis Data Streams using AWS Lambda. The team notices that some records are being processed multiple times (duplicates). Which TWO steps should the team take to ensure exactly-once processing?
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
✓
Design the Lambda function to be idempotent.
Options A and B are correct. Making the Lambda function idempotent ensures that processing the same record multiple times does not cause duplicates downstream. Using a unique identifier per record and storing processed IDs in an external store like DynamoDB allows deduplication by checking if a record has already been processed. Option C is incorrect because increasing batch size does not prevent duplicates and may increase the chance of processing failures. Option D is incorrect because KPL provides exactly-once delivery to Kinesis Data Streams, not from the stream to Lambda, so deduplication is still needed. Option E is incorrect because disabling retries can lead to data loss without guaranteeing exactly-once processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Design the Lambda function to be idempotent.
Why this is correct
Idempotency ensures repeated processing produces same result.
- ✓
Use a unique record identifier and store processed IDs in an external store like DynamoDB.
Why this is correct
Deduplication using external store prevents duplicates.
- ✗
Increase the batch size to reduce the number of invocations.
Why it's wrong here
Larger batches may increase duplicates.
- ✗
Use Kinesis Producer Library (KPL) to guarantee exactly-once delivery.
Why it's wrong here
KPL does not guarantee exactly-once; deduplication is still needed.
- ✗
Disable retries on the Lambda function.
Why it's wrong here
Disabling retries may lead to data loss.
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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