SAP-C02 Design for New Solutions Practice Question
A company is designing a new data lake on AWS using Amazon S3. The data will be ingested from various sources, including IoT devices, application logs, and streaming data. The data must be processed in near real-time as it arrives. Which combination of services should be used for ingestion and processing?
⚠ Common exam trap
Many candidates confuse Amazon S3 Transfer Acceleration (a speed optimization for large file uploads) with a streaming ingestion service, or assume that Athena can process data as it arrives, when in fact Athena only queries data at rest in S3.
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 Kinesis Data Firehose and Amazon Kinesis Data Analytics
Amazon Kinesis Data Firehose is the correct ingestion service because it can reliably capture and load streaming data into Amazon S3 in near real-time without custom code. Amazon Kinesis Data Analytics then processes the data using SQL or Apache Flink as it arrives, enabling near real-time transformations and analytics before the data lands in the data lake.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon S3 Transfer Acceleration and AWS Lambda
Why it's wrong here
S3 Transfer Acceleration is for fast uploads, not for streaming. Lambda can process data but not as a continuous ingestion pipeline.
- ✓
Amazon Kinesis Data Firehose and Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Firehose can ingest streaming data and deliver it to S3 for the data lake. Kinesis Data Analytics can process the data in near real-time.
- ✗
Amazon Athena and Amazon S3
Why it's wrong here
Athena is an interactive query service for querying data in S3, not for ingestion. It does not process data in real-time.
- ✗
AWS Glue and Amazon Redshift
Why it's wrong here
AWS Glue is a batch ETL service, not suitable for near real-time ingestion. Redshift is a data warehouse, not a data lake.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAP-C02 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 SAP-C02 exam.