MLS-C01 Data Engineering Practice Question
A company is building a data pipeline that uses Amazon Kinesis Data Streams to ingest real-time events. The pipeline then uses AWS Lambda to process the events and store results in Amazon DynamoDB. The company wants to ensure that the Lambda function can process all events without data loss and without duplicating processing. Which TWO configuration steps should the company take?
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 data retention period of the Kinesis stream to 7 days to allow reprocessing
To ensure all events are processed without data loss and without duplicating processing, the company should increase the Kinesis stream retention to 7 days (option A). This allows reprocessing of events that fail initial processing, thus preventing data loss. The company should also configure the Lambda function to disable retries on failure (option E). Automatic retries can cause the same batch of events to be processed multiple times, leading to duplication unless the Lambda function is idempotent. By disabling retries, the company can handle failures manually, ensuring exactly-once processing when combined with idempotent reprocessing logic. Option B (reducing batch window) does not prevent duplicates and may increase invocations. Option C (enabling iterator age metric) is for monitoring, not preventing loss/duplication. Option D (single shard) does not prevent duplication and can cause throughput limitations.
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 data retention period of the Kinesis stream to 7 days to allow reprocessing
Why this is correct
Correct. Increasing retention allows reprocessing of events, ensuring no data loss.
- ✗
Set the Lambda function's batch window to a small value (e.g., 1 second) to reduce processing latency
Why it's wrong here
Incorrect. Reducing batch window does not prevent duplicate processing; it may increase invocations without guaranteeing exactly-once.
- ✗
Enable the 'iterator age' metric in Amazon CloudWatch to monitor consumer lag
Why it's wrong here
Incorrect. Monitoring iterator age helps detect consumer lag but does not prevent data loss or duplication.
- ✗
Use a single shard for the Kinesis stream to ensure order and avoid parallel processing issues
Why it's wrong here
Incorrect. Using a single shard does not prevent data loss or duplicate processing, and can limit throughput.
- ✓
Configure the Lambda function to disable retries on failure to avoid duplicate processing
Why this is correct
Correct. Disabling retries prevents automatic duplicate invocations, allowing manual reprocessing to ensure exactly-once.
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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