The correct answer is Amazon Kinesis Data Streams for ingestion and Amazon Kinesis Data Analytics for Apache Flink for processing. Kinesis Data Streams is the ideal ingestion layer because it provides durable, scalable shards that can handle the high throughput from thousands of IoT devices while retaining data for up to 365 days, enabling Apache Flink to consume records with exactly-once semantics and low latency. On the SAP-C02 exam, this pairing tests your understanding of real-time streaming ingestion and Apache Flink processing within AWS’s managed ecosystem, often appearing in scenarios that require decoupling data capture from compute. A common trap is selecting Amazon MSK or Kinesis Data Firehose—MSK adds operational overhead for Kafka management, while Firehose lacks the replay and fine-grained consumption needed for Flink’s stateful processing. Remember the mnemonic “Streams for streams, Analytics for Flink” to quickly recall that Kinesis Data Streams handles ingestion, and Kinesis Data Analytics for Apache Flink handles the processing logic.
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 new application that will process streaming data from thousands of IoT devices. The data must be ingested in real time and then processed using Apache Flink. Which services should be used? (Choose TWO.)
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 Streams is the correct ingestion service because it provides a durable, scalable, real-time data streaming platform that can handle the high throughput from thousands of IoT devices. It stores data in shards for up to 365 days, enabling Apache Flink to consume and process the data with exactly-once semantics and low latency.
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.
✓
Amazon Kinesis Data Streams
Why this is correct
Kinesis Data Streams is designed for real-time data ingestion.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Lambda
Why it's wrong here
Lambda can process streaming data but does not natively support Apache Flink.
✓
Amazon Kinesis Data Analytics for Apache Flink
Why this is correct
This service allows running Apache Flink applications on Kinesis data streams.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose is for loading data to destinations like S3 or Redshift, not for Flink processing.
✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
SQS is a message queue, not designed for streaming ingestion.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Kinesis Data Firehose with Kinesis Data Streams, not realizing that Firehose is a delivery service that does not support Apache Flink's requirement for per-record replay and checkpointing, while Data Streams provides the necessary persistent, ordered stream.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the unit of throughput, each providing 1 MB/s write and 2 MB/s read capacity, and data records are stored for up to 365 days by default. Kinesis Data Analytics for Apache Flink runs a managed Flink cluster that reads from Kinesis Data Streams using the Kinesis Data Streams API, enabling stateful computations like windowed aggregations and exactly-once processing via checkpointing to an S3 bucket. In a real-world IoT scenario, this combination allows the application to handle bursty data from thousands of devices while maintaining low-latency processing for real-time alerts or analytics.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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 Streams is the correct ingestion service because it provides a durable, scalable, real-time data streaming platform that can handle the high throughput from thousands of IoT devices. It stores data in shards for up to 365 days, enabling Apache Flink to consume and process the data with exactly-once semantics and low latency.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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 is designing a new application that will process streaming data from IoT devices. The data must be ingested in real-time and stored in Amazon S3 for long-term analytics. Which AWS service should be used to ingest the streaming data?
easy
A.Amazon Simple Notification Service (SNS)
B.Amazon Simple Queue Service (SQS)
C.AWS Database Migration Service (DMS)
✓ D.Amazon Kinesis Data Streams
Why D: Option C is correct because Amazon Kinesis Data Streams is designed for real-time data ingestion. Option A is wrong because SQS is for message queues, not streaming ingestion. Option B is wrong because SNS is for pub/sub messaging. Option D is wrong because DMS is for database migration.
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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.
Question Discussion
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