CLF-C02 Cloud Technology and Services Practice Question
A company's application needs to process streaming data from IoT sensors in real time and store processed results in S3. Which AWS service is designed for ingesting and processing real-time streaming data?
⚠ Common exam trap
It's easy for candidates to confuse Amazon SQS (a message queue) with a streaming service, but SQS does not support real-time ordered processing or high-throughput data ingestion from continuous streams like IoT sensors.
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 Streams is purpose-built for ingesting and processing real-time streaming data at scale. It can continuously capture gigabytes of data per second from hundreds of thousands of sources, such as IoT sensors, and integrate with downstream consumers like AWS Lambda or Kinesis Data Analytics for real-time processing before storing results in Amazon S3.
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 SQS
Why it's wrong here
Amazon SQS is a fully managed message queue for decoupling application components, but it follows a pull-based consumption model, not a push-based streaming model. It is optimized for individual messages with a 256 KB size limit and does not support ordered message delivery across multiple consumers natively for real-time analytics. For high-throughput continuous ingestion from thousands of IoT devices, SQS would require explicit polling and scaling effort and cannot provide the replay or fan-out capabilities that a streaming data service offers.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Amazon Kinesis Data Streams is a massively scalable and durable real-time data streaming service designed to capture, store, and process data from hundreds of thousands of sources. It ingests IoT sensor data into shards, each offering 1 MB/s write and 2 MB/s read capacity, and retains records for 24 hours by default (up to 365 days) for replay. It integrates directly with Lambda, Kinesis Data Analytics, and Firehose, enabling real-time dashboards, alerts, and continuous transformation, making it the ideal choice for this use case.
- ✗
AWS Batch
Why it's wrong here
AWS Batch is a compute scheduling service designed to run containerized or batch jobs, such as cost optimization calculations, simulations, or thousands of short-running tasks to completion. It is not an ingestion service: it cannot receive a continuous stream of sensor data nor provide an API for devices to push events. Instead, it accepts job definitions and submits jobs to a managed compute environment, which aligns with batch processing, not real-time streaming ingestion from IoT sensors.
- ✗
Amazon EMR
Why it's wrong here
Amazon EMR is a big data processing service built around Hadoop and Spark, used primarily for batch analytics, ETL, and large-scale compute jobs. While it can consume streams via Spark Streaming, it is not a managed ingestion service and provides no native API for IoT devices to send data continuously. Unlike a purpose-built streaming ingestion layer, EMR lacks features such as shard-level throughput control, data retention, and replay, making it inappropriate for real-time sensor ingestion.
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
About these practice questions
One of 988 original CLF-C02 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CLF-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 CLF-C02 exam.