- A
Feature importance analysis
Why wrong: Feature importance is for model explainability, not bias detection.
- B
Amazon Rekognition
Why wrong: Rekognition is for image and video analysis, unrelated to tabular bias detection.
- C
Model Monitor
Why wrong: Model Monitor is for monitoring deployed models, not for data preparation.
- D
Built-in bias detection with SageMaker Clarify
Data Wrangler includes a bias report powered by Clarify that checks for bias in the dataset.
Question 46 of 1,000
mediumMultiple ChoiceObjective-mapped
Bias Detection in Data Wrangler Using SageMaker Clarify
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 uses Amazon SageMaker Data Wrangler for data preparation. The data science team wants to automatically detect potential bias in their dataset before training a model. Which feature of Data Wrangler should they 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
✓
Built-in bias detection with SageMaker Clarify
Amazon SageMaker Data Wrangler includes built-in bias detection powered by SageMaker Clarify. This feature allows data scientists to analyze datasets for potential bias before model training, directly within the Data Wrangler visual interface. Option D is correct because it is the only option that provides automated bias detection at the data preparation stage.
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.
- ✗
Feature importance analysis
Why it's wrong here
Feature importance is for model explainability, not bias detection.
- ✗
Amazon Rekognition
Why it's wrong here
Rekognition is for image and video analysis, unrelated to tabular bias detection.
- ✗
Model Monitor
Why it's wrong here
Model Monitor is for monitoring deployed models, not for data preparation.
- ✓
Built-in bias detection with SageMaker Clarify
Why this is correct
Data Wrangler includes a bias report powered by Clarify that checks for bias in the dataset.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between pre-training bias detection (Clarify in Data Wrangler) and post-training monitoring (Model Monitor), causing candidates to confuse the two services.
Detailed technical explanation
How to think about this question
SageMaker Clarify uses statistical metrics such as Class Imbalance (CI), Difference in Positive Proportions (DPPL), and Conditional Demographic Disparity (CDD) to quantify bias. In Data Wrangler, these metrics are computed directly on the DataFrame without requiring a trained model, enabling early detection of skewed distributions or sensitive attribute imbalances. A real-world scenario is detecting gender bias in a hiring dataset before training a resume screening model, where Clarify can flag disproportionate representation across groups.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Built-in bias detection with SageMaker Clarify — Amazon SageMaker Data Wrangler includes built-in bias detection powered by SageMaker Clarify. This feature allows data scientists to analyze datasets for potential bias before model training, directly within the Data Wrangler visual interface. Option D is correct because it is the only option that provides automated bias detection at the data preparation stage.
What should I do if I get this MLA-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 →
Same concept, more angles
1 more ways this is tested on MLA-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 machine learning engineer is building an ML pipeline using Amazon SageMaker. The engineer needs to prepare the data, detect bias in the dataset, and then create features for training. Which TWO AWS services or features should the engineer use? (Choose TWO.)
medium- A.AWS Glue DataBrew
- B.Amazon SageMaker Model Monitor
- ✓ C.Amazon SageMaker Clarify
- ✓ D.Amazon SageMaker Data Wrangler
- E.Amazon SageMaker Feature Store
Why C: Amazon SageMaker Data Wrangler is the visual data preparation tool that also integrates with Clarify for bias detection. SageMaker Clarify provides bias detection and explainability. Together they cover data preparation and bias detection in the same workflow.
Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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