DEA-C01 Data Ingestion and Transformation Practice Question
A company ingests IoT sensor data into Kinesis Data Streams. The data is then processed by a Lambda function that aggregates readings and writes to DynamoDB. The Lambda function is experiencing high error rates due to throttling. Which TWO actions would reduce throttling?
⚠ Common exam trap
Many candidates assume increasing shards (Option A) always improves throughput, but in a Lambda-integrated Kinesis stream, more shards mean more concurrent invocations, which can actually increase throttling risk.
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 batch size in the Lambda event source mapping.
Increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of concurrent Lambda invocations and thus lowering the risk of throttling. Option D is correct because switching DynamoDB to on-demand capacity mode eliminates write capacity limits, preventing throttling on the DynamoDB side that can cause Lambda retries and backpressure.
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 number of shards in the Kinesis stream.
Why it's wrong here
More shards increase parallelism but do not directly reduce Lambda throttling.
- ✓
Increase the batch size in the Lambda event source mapping.
Why this is correct
Larger batches mean fewer invocations, reducing throttling.
- ✗
Decrease the batch window in the Lambda event source mapping.
Why it's wrong here
Shorter windows increase invocations, worsening throttling.
- ✓
Configure DynamoDB to use on-demand capacity mode.
Why this is correct
On-demand mode eliminates write throttling from DynamoDB.
- ✗
Increase the Lambda reserved concurrency to 1000.
Why it's wrong here
Reserved concurrency sets a limit but does not prevent throttling if the account limit is exceeded.
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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