DEA-C01 Data Operations and Support Practice Question
A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by an AWS Lambda function that processes records and stores results in Amazon DynamoDB. Recently, the Lambda function has been failing with ProvisionedThroughputExceededException errors. Which action should the data engineer take to resolve this issue?
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
✓
Enable auto scaling on the DynamoDB table to handle increased write capacity.
Enabling DynamoDB auto scaling dynamically adjusts throughput to match demand. Option B is wrong because reducing the number of shards would lower the ingestion rate, which is not appropriate for handling high traffic. Option C is wrong because increasing the batch size in the Lambda event source mapping would process more records per invocation, but the underlying issue is DynamoDB throughput, not batch size. Option D is wrong because discarding records that cause throttling would lead to data loss.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable auto scaling on the DynamoDB table to handle increased write capacity.
Why this is correct
Auto scaling adjusts throughput based on actual usage, preventing throttling.
- ✗
Reduce the number of shards in the Kinesis stream to lower the ingestion rate.
Why it's wrong here
Reducing shards decreases throughput, but the root cause is DynamoDB capacity, not ingestion rate.
- ✗
Increase the batch size in the Lambda event source mapping to process more records per invocation.
Why it's wrong here
Larger batch sizes increase write requests per invocation, making throttling worse.
- ✗
Configure the Lambda function to discard records that cause throttling errors.
Why it's wrong here
Discarding records leads to data loss and is not a recommended practice.
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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Written by Johnson Ajibi, MSc IT Security
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