- A
Use AWS Glue DataBrew to create data quality rules that check for missing values, duplicates, and outliers, and schedule them to run regularly.
DataBrew has built-in data quality functionalities.
- B
Use Amazon Kinesis Data Analytics to continuously monitor streaming data for data quality issues.
Why wrong: The requirement is for historical batch data, not streaming.
- C
Create Amazon CloudWatch alarms based on the data quality metrics and trigger Amazon SNS notifications when thresholds are breached.
CloudWatch alarms + SNS provide alerting.
- D
Implement the Deequ library on Amazon EMR to compute data quality metrics and store them in Amazon CloudWatch.
Deequ is a popular library for data quality checks on Spark.
- E
Use Amazon SageMaker Processing jobs to run custom data quality scripts and store results in SageMaker Experiments.
Why wrong: SageMaker Processing is for ML preprocessing, not ongoing data quality monitoring.
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 company runs a data lake on Amazon S3 with AWS Glue for ETL. The data science team needs to train machine learning models on historical data, but they are concerned about data quality issues such as missing values, duplicates, and outliers. The team wants to build a data quality monitoring solution that automatically detects anomalies and alerts the data engineering team. Which THREE steps should the team take to implement this solution? (Choose THREE.)
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 AWS Glue DataBrew to create data quality rules that check for missing values, duplicates, and outliers, and schedule them to run regularly.
Option A is correct because AWS Glue DataBrew provides built-in data quality checks and profiling. Option C is correct because Deequ is an open-source library that can run on Amazon EMR or Glue to compute data quality metrics. Option D is correct because CloudWatch alarms can be set on custom metrics to send alerts via SNS. Option B is incorrect because SageMaker is for model training, not data quality monitoring. Option E is incorrect because Kinesis Data Analytics is for real-time streaming analytics, not batch data quality checks.
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.
- ✓
Use AWS Glue DataBrew to create data quality rules that check for missing values, duplicates, and outliers, and schedule them to run regularly.
Why this is correct
DataBrew has built-in data quality functionalities.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon Kinesis Data Analytics to continuously monitor streaming data for data quality issues.
Why it's wrong here
The requirement is for historical batch data, not streaming.
- ✓
Create Amazon CloudWatch alarms based on the data quality metrics and trigger Amazon SNS notifications when thresholds are breached.
Why this is correct
CloudWatch alarms + SNS provide alerting.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement the Deequ library on Amazon EMR to compute data quality metrics and store them in Amazon CloudWatch.
Why this is correct
Deequ is a popular library for data quality checks on Spark.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon SageMaker Processing jobs to run custom data quality scripts and store results in SageMaker Experiments.
Why it's wrong here
SageMaker Processing is for ML preprocessing, not ongoing data quality monitoring.
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.
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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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: Use AWS Glue DataBrew to create data quality rules that check for missing values, duplicates, and outliers, and schedule them to run regularly. — Option A is correct because AWS Glue DataBrew provides built-in data quality checks and profiling. Option C is correct because Deequ is an open-source library that can run on Amazon EMR or Glue to compute data quality metrics. Option D is correct because CloudWatch alarms can be set on custom metrics to send alerts via SNS. Option B is incorrect because SageMaker is for model training, not data quality monitoring. Option E is incorrect because Kinesis Data Analytics is for real-time streaming analytics, not batch data quality checks.
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
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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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