- A
Use AWS Lambda functions with Kinesis triggers to process each record, join across shards using a DynamoDB table for state, and write to S3.
Why wrong: Lambda processes each shard independently; cross-shard joining would require complex state management and is inefficient.
- B
Use Amazon Kinesis Data Firehose to buffer the data and write to S3, then use Amazon Athena to join the data after it is stored.
Why wrong: Kinesis Data Firehose cannot perform joins; Athena is batch and would not provide near real-time results.
- C
Use AWS Glue ETL jobs that read from the Kinesis stream via the Kinesis connector and write the joined results to S3.
Why wrong: AWS Glue ETL is batch-oriented and not ideal for near real-time streaming joins.
- D
Use Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 using a sink connector.
Kinesis Data Analytics for Apache Flink supports stateful stream processing and can join across shards natively.
Quick Answer
The correct answer is to use Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 via a sink connector. This solution is ideal because Apache Flink on KDA is purpose-built for stateful stream processing, enabling a real-time join across Kinesis shards without requiring you to manage the underlying infrastructure or coordinate state between shards manually. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of which AWS service handles near real-time, stateful joins with the least operational overhead—a common trap is confusing Kinesis Data Firehose (which cannot join) or Lambda (which processes shards independently) with Flink’s ability to maintain a unified, fault-tolerant state across all shards. Remember: for any streaming join that must correlate events from multiple shards in real time, think Flink on KDA, not Firehose or Lambda. Memory tip: “Flink joins across shards—Firehose just forwards.”
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 streaming e-commerce events to Amazon Kinesis Data Streams. The data science team needs to join events from multiple shards in near real-time and then store the joined results in Amazon S3. Which solution would meet these requirements with the LEAST operational overhead?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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 Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 using a sink connector.
Option C is correct because Amazon Kinesis Data Analytics for Apache Flink can read from a Kinesis stream, perform stateful joins across shards using Flink's SQL or DataStream API, and write results to S3. Option A is wrong because while Glue ETL can process data, it is batch-oriented and not designed for near real-time streaming joins. Option B is wrong because Lambda with Kinesis triggers processes each shard independently; joining across shards would require external state management and is not a typical pattern. Option D is wrong because Kinesis Data Firehose cannot perform joins; it only writes data to destinations.
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.
- ✗
Use AWS Lambda functions with Kinesis triggers to process each record, join across shards using a DynamoDB table for state, and write to S3.
Why it's wrong here
Lambda processes each shard independently; cross-shard joining would require complex state management and is inefficient.
- ✗
Use Amazon Kinesis Data Firehose to buffer the data and write to S3, then use Amazon Athena to join the data after it is stored.
Why it's wrong here
Kinesis Data Firehose cannot perform joins; Athena is batch and would not provide near real-time results.
- ✗
Use AWS Glue ETL jobs that read from the Kinesis stream via the Kinesis connector and write the joined results to S3.
Why it's wrong here
AWS Glue ETL is batch-oriented and not ideal for near real-time streaming joins.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 using a sink connector.
Why this is correct
Kinesis Data Analytics for Apache Flink supports stateful stream processing and can join across shards natively.
Clue confirmation
The clue word "least" in the question point toward this answer.
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.
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: Use Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 using a sink connector. — Option C is correct because Amazon Kinesis Data Analytics for Apache Flink can read from a Kinesis stream, perform stateful joins across shards using Flink's SQL or DataStream API, and write results to S3. Option A is wrong because while Glue ETL can process data, it is batch-oriented and not designed for near real-time streaming joins. Option B is wrong because Lambda with Kinesis triggers processes each shard independently; joining across shards would require external state management and is not a typical pattern. Option D is wrong because Kinesis Data Firehose cannot perform joins; it only writes data to destinations.
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.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
This MLS-C01 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 MLS-C01 exam.
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