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
Transfer Acceleration only speeds up uploads to S3 over long distances; it does not ingest streaming or IoT data, and Lambda needs an event source to trigger on. Tempting because both are real S3-adjacent services, and Transfer Acceleration would suit accelerating large global uploads, but neither provides near-real-time stream ingestion.
- ✓
Amazon Kinesis Data Firehose and Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Firehose ingests streaming and log data continuously, while Kinesis Data Analytics runs SQL over the stream for near real-time processing. Together they satisfy the low-latency processing constraint across IoT, logs and streaming sources before landing data in S3.
- ✗
Amazon Athena and Amazon S3
Why it's wrong here
Athena queries data already in S3 using SQL; it cannot ingest IoT or streaming data, and it is not a processing engine for arriving records. Tempting because it is a genuine S3 analytics service, and would be correct for ad-hoc or scheduled queries over a static data lake, but the stem demands near-real-time ingestion and processing.
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AWS Glue and Amazon Redshift
Why it's wrong here
Glue is a batch-oriented ETL service and Redshift is a warehouse, so neither processes records as they arrive. Tempting because both are legitimate data-lake components, and Glue with Redshift would be correct for scheduled batch transformation into a warehouse, but the stem requires near-real-time streaming ingestion and processing.
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 |
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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.