Quick Answer
The answer is Amazon Kinesis Data Analytics, as it is purpose-built for real-time stream processing with SQL or Apache Flink, directly matching the need to transform streaming data on the fly before landing results in Amazon S3. Unlike Kinesis Data Streams, which only ingests raw data, or Kinesis Data Firehose, which simply loads data into destinations without processing logic, Kinesis Data Analytics allows you to run continuous queries and stateful computations against live streams. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this question tests your ability to distinguish between the Kinesis family services based on their specific roles in a streaming pipeline—a common trap is confusing ingestion (Data Streams) or delivery (Firehose) with actual processing. Remember the memory tip: “Analytics does the thinking, Streams does the drinking, Firehose does the sinking.”
This SAP-C02 practice question tests your understanding of design for new solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 building a new data analytics platform on AWS. The platform ingests streaming data from multiple sources, processes it in real time, and stores the results in Amazon S3 for later analysis. The data volume is expected to be up to 50 GB per day. The company needs to choose a service for real-time stream processing. Which AWS service is most appropriate for this use case?
Amazon Kinesis Data Firehose
Why wrong: Firehose loads data into destinations; it does not perform real-time processing.
Amazon Kinesis Data Streams
Why wrong: Data Streams ingests data but does not process it; you need a consumer.
Amazon Kinesis Data Analytics
Kinesis Data Analytics processes streaming data in real time.
Amazon EMR
Why wrong: EMR is for batch processing, not real-time streaming.
Answer choices
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 Analytics
Option A is correct because Amazon Kinesis Data Analytics enables real-time stream processing using SQL or Apache Flink. Option B: Kinesis Data Streams is for data ingestion, not processing. Option C: Kinesis Data Firehose is for loading data into stores, not for real-time processing. Option D: Amazon EMR is for batch processing, not real-time.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose loads data into destinations; it does not perform real-time processing.
Amazon Kinesis Data Streams
Why it's wrong here
Data Streams ingests data but does not process it; you need a consumer.
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics processes streaming data in real time.
Related concept
Read the scenario before looking for a memorised answer.
Amazon EMR
Why it's wrong here
EMR is for batch processing, not real-time streaming.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
Identify which SAP-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Design for New Solutions — This question tests Design for New Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Kinesis Data Analytics — Option A is correct because Amazon Kinesis Data Analytics enables real-time stream processing using SQL or Apache Flink. Option B: Kinesis Data Streams is for data ingestion, not processing. Option C: Kinesis Data Firehose is for loading data into stores, not for real-time processing. Option D: Amazon EMR is for batch processing, not real-time.
Identify which SAP-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
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 data analytics company is building a real-time streaming pipeline using Amazon Kinesis Data Streams. The data is consumed by multiple consumer applications, each with different processing requirements. The company wants to ensure that each consumer can process records independently without affecting others and can reprocess data from a specific point in time. Which feature should the company use?
mediumWhy A: Enhanced Fan-Out (EFO) provides each consumer with a dedicated 2 MB/second read throughput per shard, ensuring independent processing without contention. By using the SubscribeToShard API with a starting position specified via a timestamp, consumers can reprocess data from a specific point in time, meeting the requirement exactly.
Variation 2. A company is designing a real-time analytics pipeline for IoT data. They need to ingest millions of messages per second, process them with low latency, and store results in Amazon S3. Which combination of services should they use?
mediumWhy A: Option A is correct because Kinesis Data Streams ingests high-throughput data, Kinesis Data Analytics processes it in real-time, and Firehose delivers to S3. Option B uses SQS which is not designed for millions of messages per second. Option C uses Lambda which may throttle. Option D uses Redshift which is for data warehousing, not real-time streaming.
Variation 3. A company is designing a new real-time analytics platform that ingests millions of events per second from IoT devices. The events must be processed with low latency (under 100 ms) and stored for replay. The company wants to use managed services. Which combination of AWS services should the company use?
hardWhy A: Amazon Kinesis Data Streams can ingest and buffer millions of events per second with sub-100 ms latency, and AWS Lambda can process each record in near real-time as it arrives in the stream. This combination meets the low-latency requirement and allows events to be replayed from the stream's retention window (up to 365 days with extended retention).
Variation 4. A company is designing a real-time analytics platform that ingests data from thousands of IoT devices. The platform must process and store high-velocity data with low latency. Which TWO AWS services should be used together to meet these requirements? (Choose TWO.)
mediumWhy B: Amazon Kinesis Data Streams ingests high-velocity streaming data. Amazon Kinesis Data Analytics performs real-time analytics on the stream. Option B (Lambda) can be used but not as the primary analytics engine. Option C (S3) is for storage, not real-time. Option D (DynamoDB) is for low-latency queries but not for stream analytics. Option E (SQS) is for message queuing, not real-time analytics.
Variation 5. A company is designing a real-time analytics platform that ingests data from thousands of IoT devices. Each device sends a JSON payload every second. The company needs to store the raw data for a month and then aggregate it into hourly summaries for long-term storage. The solution must be serverless and cost-effective. Which combination of AWS services should the company use?
hardWhy B: Option D is correct because Kinesis Data Streams ingests real-time data, Kinesis Data Analytics performs real-time aggregation, and Firehose delivers to S3. Option A uses Lambda for aggregation, which is not ideal for streaming aggregations. Option B uses SQS, which is not designed for real-time streaming. Option C misses the real-time aggregation step.
Variation 6. A company is designing a real-time analytics pipeline to process streaming data from IoT devices. The solution must be serverless and handle data transformation before storage. Which combination of services is most cost-effective?
mediumWhy A: Option B is correct because Kinesis Data Streams ingests data, Lambda transforms it, and Firehose delivers to S3. Option A (Kinesis Analytics) is for SQL analytics, not transformation. Option C (Kinesis Data Streams to EC2) is not serverless. Option D (SQS to Lambda to S3) misses streaming ingestion.
Variation 7. A company is designing a new real-time analytics platform that processes streaming data from IoT devices. The data must be ingested, processed with windowed aggregations, and stored in Amazon S3 for long-term analytics. The solution must handle late-arriving data and provide exactly-once processing semantics. Which combination of AWS services should the architect use?
hardWhy D: Kinesis Data Analytics for Apache Flink (option D) provides exactly-once processing and handles late data. Option A (Kinesis Data Firehose) does not provide exactly-once. Option B (Lambda) is not ideal for streaming analytics. Option C (EMR) is more complex.
Last reviewed: Jun 20, 2026
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