MLS-C01 Data Engineering Practice Question
A company is using Amazon Kinesis Data Streams with 10 shards to ingest clickstream data. Each record is approximately 50 KB. The data is consumed by a Lambda function that writes to DynamoDB. The Lambda function is experiencing throttling errors. Which TWO actions should the data engineer take to resolve the issue? (Choose TWO.)
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
✓
Request a limit increase for the Lambda function's concurrent execution limit
The Lambda function is experiencing throttling errors because it is being invoked too frequently. To resolve this, the data engineer should increase the Lambda function's concurrent execution limit (option C) to allow more simultaneous executions, and increase the batch size in the Lambda event source mapping (option E) to process more records per invocation, reducing the number of invocations. Option A (increase record size) is irrelevant as it would increase data volume. Option B (switch to Kinesis Data Firehose) changes the architecture and does not directly address Lambda throttling. Option D (increase the number of shards) would increase throughput but also potentially increase concurrency without solving the throttling issue. Therefore, the correct answers are C and E.
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 record size to 1 MB to reduce the number of records
Why it's wrong here
Record size is determined by the data source; artificially increasing it is not feasible.
- ✗
Switch to Kinesis Data Firehose instead of Data Streams
Why it's wrong here
Firehose does not support Lambda as a direct consumer with the same flexibility.
- ✓
Request a limit increase for the Lambda function's concurrent execution limit
Why this is correct
This directly alleviates throttling by allowing more concurrent executions.
- ✗
Increase the number of shards in the Kinesis stream
Why it's wrong here
More shards increase parallelism but also increase Lambda invocations, potentially worsening throttling.
- ✓
Increase the batch size in the Lambda event source mapping
Why this is correct
Larger batches mean fewer Lambda invocations, reducing concurrency.
Visual reference
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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