- A
AWS Lambda, Amazon DynamoDB Streams, and Amazon S3.
Why wrong: DynamoDB Streams is for change data capture from DynamoDB, not for ingesting external streaming data.
- B
Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, and Amazon Kinesis Data Firehose.
Kinesis Data Streams ingests streaming data, Kinesis Data Analytics processes it in real-time, and Firehose loads it into S3.
- C
Amazon SQS, AWS Lambda, and Amazon S3.
Why wrong: SQS is a message queue, not designed for streaming data; Lambda can poll SQS but is not ideal for high-throughput streaming.
- D
Amazon Kinesis Data Firehose, AWS Glue, and Amazon S3.
Why wrong: Glue is for batch ETL, not near real-time processing.
Question 859 of 1,746
SAP-C02 Design for New Solutions Practice Question
This SAP-C02 practice question tests your understanding of design for new solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards to partition data, each providing 1 MB/s write and 2 MB/s read throughput, and records are stored for up to 365 days for replay. Kinesis Data Analytics can run Apache Flink applications that process data with exactly-once semantics, enabling complex event processing like sliding window averages. Kinesis Data Firehose can batch and compress data before writing to S3, reducing storage costs and enabling downstream querying with services like Athena.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this SAP-C02 question test?
Design for New Solutions — This question tests Design for New Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, and Amazon Kinesis Data Firehose. — Option B is correct because 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.
What should I do if I get this SAP-C02 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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