DEA-C01 Data Store Management Practice Question
A data engineer is designing a real-time analytics solution using Amazon DynamoDB. The workload requires capturing all changes to a DynamoDB table and processing them in near-real-time to update a materialized view in Amazon Redshift. Which approach should the engineer use to capture and process the changes?
⚠ Common exam trap
Many candidates confuse DynamoDB Streams with other features like global tables or DAX, which serve different purposes such as multi-region replication or caching, and do not provide change data capture for real-time processing.
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 DynamoDB Streams and use AWS Lambda to process the stream and write to Redshift.
DynamoDB Streams captures item-level changes in near-real-time, and AWS Lambda can process these changes and write to Amazon Redshift. This serverless architecture is scalable, cost-effective, and requires minimal operational overhead. It is the standard pattern for change data capture from DynamoDB to other data stores.
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 DynamoDB global tables and use Amazon Kinesis Data Streams to replicate changes to Redshift.
Why it's wrong here
DynamoDB global tables provide multi-region replication, not change data capture for downstream processing. Kinesis Data Streams can be used with DynamoDB Streams via the Kinesis adapter, but global tables alone do not emit changes to Kinesis. This approach adds unnecessary complexity and does not directly capture changes.
- ✗
Use AWS Database Migration Service (AWS DMS) with ongoing replication from DynamoDB to Redshift.
Why it's wrong here
AWS DMS does not support DynamoDB as a source for ongoing replication to Redshift. It can migrate data from DynamoDB to other targets like S3, but not directly to Redshift with change data capture. This approach is not feasible for real-time updates.
- ✗
Use DynamoDB Accelerator (DAX) to cache changes and periodically export to Redshift.
Why it's wrong here
DAX is an in-memory cache for DynamoDB that speeds up reads, but it does not capture changes or provide a mechanism for exporting data to Redshift. It is not designed for change data capture or real-time analytics. This approach would not meet the requirement.
- ✓
Enable DynamoDB Streams and use AWS Lambda to process the stream and write to Redshift.
Why this is correct
DynamoDB Streams captures item-level changes in near-real-time. AWS Lambda can be triggered by the stream to process records and write to Redshift. This serverless approach is scalable and requires minimal operational overhead. It is the recommended pattern for real-time change data capture from DynamoDB.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.