The answer is Amazon Kinesis Data Streams with AWS Lambda and Amazon S3. This combination works because Kinesis Data Streams provides the low-latency ingestion required for streaming data from IoT devices, while AWS Lambda applies custom transformation logic in real-time as records are consumed from the stream, and the transformed data is then written to Amazon S3 with partitioning by device ID and timestamp using Lambda’s ability to specify S3 prefixes. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of real-time data pipeline architecture, specifically the distinction between Kinesis Data Streams (which requires a consumer like Lambda for custom processing) and Kinesis Data Firehose (which cannot partition on write and lacks native custom transformation without Lambda). A common trap is choosing Firehose for its simplicity, but remember: Firehose is for near-real-time delivery without custom partitioning, while Streams plus Lambda gives you full control over transformation and S3 folder structure. Memory tip: “Streams for custom logic, Firehose for simple delivery.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data pipeline to process streaming data from IoT devices. The data must be ingested with low latency, transformed in real-time using custom logic, and stored in Amazon S3 partitioned by device ID and timestamp. Which combination of AWS services should the company use to meet these requirements?
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 with AWS Lambda and Amazon S3
Option B is correct because Amazon Kinesis Data Streams provides low-latency ingestion, AWS Lambda can apply custom transformation logic in real-time, and Amazon S3 with partitioning can store the data. Option A is wrong because Kinesis Data Firehose does not support custom transformation without Lambda and cannot partition on write. Option C is wrong because Amazon MSK (Managed Streaming for Kafka) is more complex than needed and not as tightly integrated. Option D is wrong because Amazon DynamoDB Streams is not designed for this volume of streaming data.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 Firehose with direct S3 delivery
Why it's wrong here
Kinesis Firehose does not provide built-in custom transformation without Lambda and cannot partition output dynamically.
✗
Amazon Managed Streaming for Apache Kafka (MSK) with Amazon S3 sink connector
Why it's wrong here
MSK introduces operational overhead and is not the simplest solution for this use case.
✗
Amazon DynamoDB Streams with AWS Lambda and Amazon S3
Why it's wrong here
DynamoDB Streams is for change data capture from DynamoDB, not for IoT streaming data.
✓
Amazon Kinesis Data Streams with AWS Lambda and Amazon S3
Why this is correct
Kinesis Data Streams for ingestion, Lambda for real-time transformation, and S3 for storage with partitioning.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Command / output trap
Kinesis Firehose does not provide built-in custom transformation without Lambda and cannot partition output dynamically.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Data Engineering — This question tests Data Engineering — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Streams with AWS Lambda and Amazon S3 — Option B is correct because Amazon Kinesis Data Streams provides low-latency ingestion, AWS Lambda can apply custom transformation logic in real-time, and Amazon S3 with partitioning can store the data. Option A is wrong because Kinesis Data Firehose does not support custom transformation without Lambda and cannot partition on write. Option C is wrong because Amazon MSK (Managed Streaming for Kafka) is more complex than needed and not as tightly integrated. Option D is wrong because Amazon DynamoDB Streams is not designed for this volume of streaming data.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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