- A
Amazon Kinesis Data Analytics
Kinesis Data Analytics supports SQL queries on streaming data for real-time analysis.
- B
Amazon SageMaker
Why wrong: SageMaker is for building and training ML models, not streaming queries.
- C
AWS Glue
Why wrong: Glue is for batch ETL jobs, not real-time SQL.
- D
Amazon Athena
Why wrong: Athena is for querying static data in S3, not streaming data.
Question 1,577 of 1,755
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
An organization stores streaming data in Amazon Kinesis Data Streams. A data analyst wants to perform real-time exploratory data analysis on the incoming data to detect anomalies. Which AWS service should the analyst use to run SQL queries on the streaming data?
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 Analytics
Option A is correct because Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time, which is exactly what the data analyst needs for real-time exploratory data analysis and anomaly detection. Option B (Amazon SageMaker) is incorrect because it is a machine learning service for building and training models, not for running SQL on streaming data. Option C (AWS Glue) is incorrect because it is a serverless ETL service for batch processing, not real-time SQL. Option D (Amazon Athena) is incorrect because it is an interactive query service for analyzing data in S3 using SQL, but it is designed for batch queries on static data, not streaming data.
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.
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics supports SQL queries on streaming data for real-time analysis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker is for building and training ML models, not streaming queries.
- ✗
AWS Glue
Why it's wrong here
Glue is for batch ETL jobs, not real-time SQL.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying static data in S3, not streaming data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Analytics — Option A is correct because Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time, which is exactly what the data analyst needs for real-time exploratory data analysis and anomaly detection. Option B (Amazon SageMaker) is incorrect because it is a machine learning service for building and training models, not for running SQL on streaming data. Option C (AWS Glue) is incorrect because it is a serverless ETL service for batch processing, not real-time SQL. Option D (Amazon Athena) is incorrect because it is an interactive query service for analyzing data in S3 using SQL, but it is designed for batch queries on static data, not streaming data.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
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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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