SAP-C02 Design for New Solutions Practice Question
A company is designing a data lake on Amazon S3. The data will be ingested from various sources, including streaming data from IoT devices. The data must be processed in near real-time to derive insights. The company wants to use serverless technologies to minimize operational overhead. Which combination of services should the company use?
⚠ Common exam trap
Test-takers frequently confuse batch ETL (AWS Glue) or simple message queuing (SQS) with true streaming analytics, missing that Kinesis Data Analytics is the only option that provides native, serverless, near real-time stream processing with stateful operations like windowing and aggregations.
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 Streams, Amazon Kinesis Data Analytics, and Amazon Kinesis Data Firehose.
Amazon Kinesis Data Streams ingests and stores streaming IoT data durably, Amazon Kinesis Data Analytics performs near real-time processing using SQL or Apache Flink, and Amazon Kinesis Data Firehose delivers the processed data to Amazon S3 for the data lake. This combination is fully serverless, scales automatically, and meets the near real-time requirement without managing any infrastructure.
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 Lambda, Amazon DynamoDB Streams, and Amazon S3.
Why it's wrong here
DynamoDB Streams is for change data capture from DynamoDB, not for ingesting external streaming data.
- ✓
Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, and Amazon Kinesis Data Firehose.
Why this is correct
Kinesis Data Streams ingests streaming data, Kinesis Data Analytics processes it in real-time, and Firehose loads it into S3.
- ✗
Amazon SQS, AWS Lambda, and Amazon S3.
Why it's wrong here
SQS is a message queue, not designed for streaming data; Lambda can poll SQS but is not ideal for high-throughput streaming.
- ✗
Amazon Kinesis Data Firehose, AWS Glue, and Amazon S3.
Why it's wrong here
Glue is for batch ETL, not near real-time 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 |
Go deeper
Related to this question
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Same concept, more angles
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Variation 1. 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?
medium- A.Amazon S3 Transfer Acceleration and AWS Lambda
- ✓ B.Amazon Kinesis Data Firehose and Amazon Kinesis Data Analytics
- C.Amazon Athena and Amazon S3
- D.AWS Glue and Amazon Redshift
Why B: 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.
JA
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.