DEA-C01 Data Operations and Support Practice Question
A data pipeline uses AWS Lambda to process records from an Amazon Kinesis Data Stream. The Lambda function is idempotent and runs once per record. Recently, the function started failing with 'ProvisionedThroughputExceededException' when writing to a DynamoDB table. Which action should the data engineer take to resolve this?
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
✓
Implement retry logic with exponential backoff in the Lambda function.
Implementing retry logic with exponential backoff allows the Lambda function to handle transient 'ProvisionedThroughputExceededException' errors by retrying write operations with increasing delays, which reduces the load on DynamoDB and gives the table capacity to recover. Since the function is idempotent, retries are safe. Option A is incorrect: decreasing the batch size reduces the number of records per invocation but does not directly address DynamoDB throttling; it may even increase the number of concurrent invocations, potentially worsening the issue. Option B is incorrect: increasing reserved concurrency allows more Lambda functions to run concurrently, which would increase the write rate to DynamoDB, exacerbating throttling. Option D is incorrect: increasing Kinesis shards increases the stream's ingestion capacity but does not affect DynamoDB's throughput limits; the bottleneck is at the DynamoDB table, not the stream.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the Lambda function's batch size to process fewer records per invocation.
Why it's wrong here
Smaller batch size means more invocations, increasing DynamoDB write attempts.
- ✗
Increase the Lambda function's reserved concurrency.
Why it's wrong here
More concurrency means more writes to DynamoDB, making throttling worse.
- ✓
Implement retry logic with exponential backoff in the Lambda function.
Why this is correct
Exponential backoff reduces the write rate when throttled, eventually succeeding.
- ✗
Increase the number of shards in the Kinesis stream.
Why it's wrong here
More shards increase the rate of Lambda invocations, not DynamoDB throughput.
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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This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.