DEA-C01 Data Ingestion and Transformation Practice Question
A financial services company ingests real-time stock trade data from multiple exchanges into Amazon Kinesis Data Streams. Each trade record is a JSON object containing fields: trade_id, symbol, price, quantity, and timestamp. The data is consumed by an AWS Lambda function that performs data validation and enrichment, then writes the processed records to an Amazon DynamoDB table for low-latency querying. Recently, the Lambda function has been timing out and failing to process all records. The Lambda function is configured with a 5-second timeout and 128 MB memory. The average record size is 2 KB, and the stream receives about 1000 records per second. The Lambda function's concurrency limit is 1000. Which set of actions should the data engineer take to resolve the issue without losing data?
⚠ Common exam trap
The trap here is that candidates often overlook the parallelization factor setting, assuming that increasing batch size and Lambda resources alone will suffice, but without parallelization, each shard can only process one batch at a time, creating a throughput bottleneck that leads to data loss.
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 Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10.
Increasing the Lambda timeout and memory addresses the processing bottleneck, while setting the batch size to 100 and enabling a parallelization factor of 10 allows each shard to process up to 10 concurrent batches, dramatically increasing throughput to handle 1000 records/sec (each shard can process 10 batches of 100 records concurrently, yielding 1000 records/sec per shard if the stream has at least 1 shard). This combination ensures no data loss by keeping up with the ingestion rate without exceeding the Lambda concurrency limit of 1000.
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 Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10.
Why this is correct
This combination increases processing capacity and prevents timeouts.
- ✗
Increase the number of shards in the Kinesis data stream to 20 and keep the Lambda configuration unchanged.
Why it's wrong here
More shards increase parallelism but Lambda may still timeout with current settings.
- ✗
Replace the Lambda function with a Kinesis Data Firehose delivery stream that writes directly to DynamoDB using a Lambda transformation.
Why it's wrong here
Firehose does not support DynamoDB as a direct destination; it uses Lambda for transformation, which still has the same issue.
- ✗
Increase the Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records.
Why it's wrong here
This alone does not increase concurrency; may still fall behind.
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
Senior Network & Security Engineer · founder of Courseiva
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