- A
Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.
Why wrong: Glue streaming ETL is available but has higher latency and cost; it is better suited for batch near-real-time than true real-time.
- B
Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.
Why wrong: Enhanced fan-out improves throughput but does not provide built-in windowing; consumers must implement aggregation logic.
- C
Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.
Why wrong: Lambda can process streams but has limits on concurrency and execution duration; managing window state is complex and error-prone.
- D
Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.
Kinesis Data Analytics supports tumbling windows and continuous queries; Firehose is the natural sink for S3.
Streaming Windowed Aggregation with Kinesis Analytics
This MLS-C01 practice question tests your understanding of data engineering. 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 data engineering team needs to process streaming data from thousands of IoT devices. They want to aggregate data in 1-minute windows and store results in an S3 data lake for downstream analytics. Which architecture should they use?
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
Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.
Option D is correct because Kinesis Data Analytics for SQL Applications is purpose-built for real-time windowed aggregations on streaming data, such as 1-minute tumbling windows. It can directly consume from Kinesis Data Streams, perform the aggregation using standard SQL, and output the results to Kinesis Data Firehose, which reliably delivers the aggregated data to an S3 data lake with built-in buffering and compression.
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.
- ✗
Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.
Why it's wrong here
Glue streaming ETL is available but has higher latency and cost; it is better suited for batch near-real-time than true real-time.
- ✗
Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.
Why it's wrong here
Enhanced fan-out improves throughput but does not provide built-in windowing; consumers must implement aggregation logic.
- ✗
Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.
Why it's wrong here
Lambda can process streams but has limits on concurrency and execution duration; managing window state is complex and error-prone.
- ✓
Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.
Why this is correct
Kinesis Data Analytics supports tumbling windows and continuous queries; Firehose is the natural sink for S3.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Lambda is suitable for real-time windowed aggregation, overlooking its stateless nature and execution limits, while Kinesis Data Analytics is the native AWS service for this exact use case.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics for SQL uses a continuous query engine that processes streaming data in-memory using tumbling windows (e.g., GROUP BY FLOOR(rowtime TO MINUTE)). The output is sent to a Firehose delivery stream, which can batch records up to 128 MB or 900 seconds before writing to S3, ensuring efficient storage. A real-world scenario is aggregating sensor readings from thousands of IoT devices to compute average temperature per minute, where the SQL query handles late-arriving data via watermarking.
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3. — Option D is correct because Kinesis Data Analytics for SQL Applications is purpose-built for real-time windowed aggregations on streaming data, such as 1-minute tumbling windows. It can directly consume from Kinesis Data Streams, perform the aggregation using standard SQL, and output the results to Kinesis Data Firehose, which reliably delivers the aggregated data to an S3 data lake with built-in buffering and compression.
What should I do if I get this MLS-C01 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
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
3 more ways this is tested on MLS-C01
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 engineering team needs to process streaming data from thousands of IoT devices. The data must be ingested with low latency and processed in near real-time to detect anomalies. Which AWS service should they use for ingestion?
easy- A.Amazon Kinesis Data Firehose
- B.Amazon Kinesis Data Analytics
- C.Amazon S3
- ✓ D.Amazon Kinesis Data Streams
Why D: Amazon Kinesis Data Streams is the correct choice because it is designed for real-time, low-latency ingestion of streaming data from thousands of sources, such as IoT devices. It provides a durable, scalable data stream that can be consumed by multiple applications in near real-time, making it ideal for anomaly detection use cases.
Variation 2. A data science team needs to process streaming data from thousands of IoT devices and perform real-time anomaly detection. The data must be persisted in Amazon S3 for batch processing later. Which combination of AWS services should be used to meet these requirements?
medium- ✓ A.Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.
- B.Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.
- C.AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.
- D.Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.
Why A: Amazon Kinesis Data Streams provides durable, real-time ingestion for high-throughput IoT data. Kinesis Data Analytics can perform SQL-based anomaly detection on the stream, and Kinesis Data Firehose reliably delivers the processed or raw data to Amazon S3 for batch processing. This combination meets all requirements for streaming ingestion, real-time analytics, and persistent storage.
Variation 3. A data scientist needs to process a large volume of streaming data from IoT devices and store the results in Amazon S3 for further analysis. Which AWS service is most suitable for ingesting and processing this data in near real-time?
easy- A.Amazon Redshift
- B.AWS Glue
- ✓ C.Amazon Kinesis Data Analytics
- D.Amazon EMR
Why C: Amazon Kinesis Data Analytics is the most suitable service because it can process streaming data from IoT devices in near real-time using SQL or Apache Flink, and directly output the results to Amazon S3. It is designed for continuous, low-latency ingestion and analysis of data streams, making it ideal for this use case.
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Last reviewed: Jul 4, 2026
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