- A
AWS Glue with Amazon S3
Why wrong: Glue is batch-oriented, not designed for real-time streaming ingestion.
- B
Amazon SQS with AWS Lambda
Why wrong: SQS is message queue, not ideal for high-throughput streaming and lacks long retention for replay.
- C
Amazon Kinesis Data Streams with AWS Lambda
Kinesis Data Streams can ingest bursty streaming data and retain it for replay; Lambda can process and load to S3.
- D
Amazon DynamoDB Streams with AWS Lambda
Why wrong: DynamoDB Streams capture table changes, not direct IoT data ingestion.
Question 67 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 ingest streaming data from thousands of IoT devices into Amazon S3 for near-real-time analytics. The solution must handle data that arrives in bursts and must be able to reprocess failed records automatically. Which combination of AWS services should the team 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
✓
Amazon Kinesis Data Streams with AWS Lambda
Amazon Kinesis Data Streams is designed for real-time ingestion of large volumes of streaming data, such as from thousands of IoT devices, and can handle bursty traffic by scaling shards. AWS Lambda can be used as a consumer to process records in near-real-time, and Kinesis Data Streams supports automatic retries and checkpointing, enabling reprocessing of failed records without data loss.
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.
- ✗
AWS Glue with Amazon S3
Why it's wrong here
Glue is batch-oriented, not designed for real-time streaming ingestion.
- ✗
Amazon SQS with AWS Lambda
Why it's wrong here
SQS is message queue, not ideal for high-throughput streaming and lacks long retention for replay.
- ✓
Amazon Kinesis Data Streams with AWS Lambda
Why this is correct
Kinesis Data Streams can ingest bursty streaming data and retain it for replay; Lambda can process and load to S3.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon DynamoDB Streams with AWS Lambda
Why it's wrong here
DynamoDB Streams capture table changes, not direct IoT data ingestion.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse SQS with Kinesis, but SQS lacks the ordered, replayable stream semantics and high-throughput shard scaling needed for bursty IoT data ingestion and reprocessing.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards to partition data, each shard providing 1 MB/s write and 2 MB/s read capacity, allowing horizontal scaling for bursty traffic. The Lambda consumer integrates via the Kinesis Client Library (KCL), which manages checkpointing in DynamoDB, enabling automatic retries and reprocessing of failed records by rewinding the shard iterator. This architecture ensures exactly-once processing semantics within each shard, critical for IoT analytics where data loss is unacceptable.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Data Engineering — study guide chapter
Learn the concepts, then practise the questions
- →
Data Engineering practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Amazon Kinesis Data Streams with AWS Lambda — Amazon Kinesis Data Streams is designed for real-time ingestion of large volumes of streaming data, such as from thousands of IoT devices, and can handle bursty traffic by scaling shards. AWS Lambda can be used as a consumer to process records in near-real-time, and Kinesis Data Streams supports automatic retries and checkpointing, enabling reprocessing of failed records without data loss.
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 →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
Last reviewed: Jul 4, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.