DEA-C01 Data Ingestion and Transformation Practice Question
A company needs to ingest data from multiple SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data sources have different schemas and update frequencies. Which AWS service should be used to build this ingestion pipeline with minimal code?
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
✓
Amazon AppFlow
Amazon AppFlow (Option D) is the correct answer because it is purpose-built for ingesting data from SaaS applications like Salesforce and Marketo into Amazon S3 with minimal code. It supports various source connectors, handles schema variations, and allows scheduling based on update frequencies. AWS Data Pipeline (A) requires more manual configuration and code for connectors. AWS Glue (B) has connectors but is more complex for this use case, often requiring additional ETL scripting. Amazon Kinesis Data Firehose (C) is designed for streaming data, not batch extraction from SaaS APIs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Data Pipeline
Why it's wrong here
Data Pipeline is an older service; AppFlow is more modern for SaaS ingestion.
- ✗
AWS Glue
Why it's wrong here
Glue can be used but requires more configuration than AppFlow for SaaS sources.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose is for streaming data, not for batch extraction from SaaS APIs.
- ✓
Amazon AppFlow
Why this is correct
AppFlow is designed to ingest data from SaaS applications to S3 with minimal code.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Same concept, more angles
4 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company needs to ingest data from multiple SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for centralized analytics. The data volume is several GB per day. Which AWS service is most suitable for this ingestion?
easy- A.Amazon Kinesis Data Firehose
- ✓ B.AWS Glue
- C.Amazon Athena
- D.AWS Data Pipeline
Why B: AWS Glue is the most suitable service because it provides a fully managed ETL (Extract, Transform, Load) capability with built-in connectors for SaaS applications like Salesforce and Marketo. Glue can extract data from these sources, transform it as needed, and write it to Amazon S3, handling the several GB per day volume efficiently without requiring custom code for each source.
Variation 2. A company needs to ingest data from multiple SaaS applications into Amazon S3. The data sources provide REST APIs. Which AWS service can be used to build a fully managed data ingestion pipeline without writing custom code?
easy- ✓ A.Amazon AppFlow
- B.Amazon Kinesis Data Streams
- C.AWS Lambda with custom code
- D.AWS Glue with Python shell
Why A: Amazon AppFlow is a fully managed service designed to transfer data from SaaS applications to AWS services like Amazon S3 without writing custom code. Option B is wrong because Amazon Kinesis Data Streams is primarily for real-time streaming data, not for directly ingesting from SaaS APIs. Option C is wrong because AWS Lambda requires custom code to integrate with SaaS APIs. Option D is wrong because AWS Glue with Python shell is for ETL transformations and also requires custom scripting.
Variation 3. A company wants to ingest data from multiple SaaS applications into Amazon S3 using a fully managed service that supports schema discovery and transformation. Which AWS service should they use?
easy- A.Amazon Kinesis Data Firehose
- ✓ B.Amazon AppFlow
- C.AWS Glue
- D.AWS Data Pipeline
Why B: Amazon AppFlow is a fully managed integration service that supports SaaS sources, schema discovery, and data transformation. Option A (Amazon Kinesis Data Firehose) is for streaming data but not for SaaS ingestion directly. Option C (AWS Glue) is an ETL service but not for SaaS ingestion directly. Option D (AWS Data Pipeline) is not fully managed for SaaS.
Variation 4. A company wants to ingest data from SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate and updates occur frequently. Which AWS service is BEST suited for this task?
medium- A.Amazon Kinesis Data Streams
- ✓ B.Amazon AppFlow
- C.AWS Database Migration Service (DMS)
- D.AWS Glue
Why B: Amazon AppFlow is specifically designed to securely transfer data from SaaS applications like Salesforce and Marketo to AWS services such as Amazon S3. It handles authentication, data transformation, and scheduling. Amazon Kinesis Data Streams is for real-time streaming data, not batch ingestion from SaaS. AWS Database Migration Service (DMS) is for migrating databases, not SaaS data. AWS Glue is an ETL service that can process data but is not optimized for direct SaaS ingestion.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.