← MLS-C01·Amazon Web Services

Question 1,592 of 1,755

Data Engineering →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is Amazon Kinesis Data Streams for ingestion, Kinesis Data Analytics for real-time anomaly detection, and Kinesis Data Firehose to persist data to Amazon S3. This combination works because Kinesis Data Streams provides durable, scalable ingestion for high-throughput IoT data, Kinesis Data Analytics applies SQL or Apache Flink to detect anomalies in real time as the data flows through, and Firehose automatically batches and delivers the same stream to S3 for later batch processing without custom code. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the Kinesis family’s distinct roles—Streams for capture, Analytics for compute, and Firehose for delivery—and often appears as a trap where candidates confuse SQS (which lacks real-time analytics) or Lambda (which struggles with sustained high throughput). A common memory tip is “Stream, Analyze, Deliver”: think of the data as a river that must be caught (Streams), inspected for pollution (Analytics), and then bottled for storage (Firehose).

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 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?

Question 1mediummultiple choice

Read the full NAT/PAT explanation →

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.
This combination provides real-time ingestion, analytics, and durable storage.
B
Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.
Why wrong: Glue is a batch ETL service, not suitable for real-time anomaly detection.
C
AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.
Why wrong: Lambda cannot handle continuous high-throughput streaming and does not persist data to S3 natively.
D
Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.
Why wrong: SQS is not designed for high-throughput streaming and lacks real-time analytics capabilities.

Full breakdown with real-world context →

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 for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.

Option A is correct because Kinesis Data Streams ingests streaming data, Kinesis Data Analytics performs real-time anomaly detection, and Firehose delivers data to S3 for batch processing. Option B is wrong because SQS is not optimized for streaming and does not have built-in analytics. Option C is wrong because Lambda alone cannot handle high-throughput streaming and lacks persistence to S3. Option D is wrong because Glue is a batch ETL service, not real-time.

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 Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.
Why this is correct
This combination provides real-time ingestion, analytics, and durable storage.
Related concept
Static NAT maps one inside address to one outside address.
✗
Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.
Why it's wrong here
Glue is a batch ETL service, not suitable for real-time anomaly detection.
✗
AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.
Why it's wrong here
Lambda cannot handle continuous high-throughput streaming and does not persist data to S3 natively.
✗
Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.
Why it's wrong here
SQS is not designed for high-throughput streaming and lacks real-time analytics capabilities.

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.

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.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

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 for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3. — Option A is correct because Kinesis Data Streams ingests streaming data, Kinesis Data Analytics performs real-time anomaly detection, and Firehose delivers data to S3 for batch processing. Option B is wrong because SQS is not optimized for streaming and does not have built-in analytics. Option C is wrong because Lambda alone cannot handle high-throughput streaming and lacks persistence to S3. Option D is wrong because Glue is a batch ETL service, not real-time.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

About these practice questions

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Same concept, more angles

2 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. 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?

medium

A.Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.
B.Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.
C.Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.
✓ D.Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.

Why D: Option D is correct because Kinesis Data Analytics provides real-time SQL-based processing with windowing functions, and Kinesis Firehose can deliver aggregated data directly to S3. Option A is wrong because Lambda scales but has a 15-minute timeout and is not ideal for heavy streaming aggregation. Option B is wrong because Kinesis Data Streams alone does not process data; it requires a consumer. Option C is wrong because Glue is batch-oriented, not real-time.

Variation 2. 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 designed for real-time processing of streaming data. AWS Glue is for batch ETL, Amazon EMR for big data processing, and Amazon Redshift for data warehousing.

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