mediumMultiple Choice
MLA-C01 Practice Question: A company uses Amazon SageMaker Data Wrangler for…
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?
⚠ Common exam trap
AWS 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.
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.
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 ranks which input columns most influence a trained model's predictions; it does not measure bias in a dataset before training. It is tempting because it also analyses data columns, but it requires an existing model and reports predictive contribution, not imbalance across facets such as sex or age.
- ✗
Amazon Rekognition
Why it's wrong here
Rekognition performs image and video analysis such as object and face detection; it does not inspect tabular training datasets for bias. It is tempting because it is an AWS AI service with fairness-adjacent face analysis, but that applies to facial attribute comparisons, not SageMaker Data Wrangler's bias detection for tabular data.
- ✗
Model Monitor
Why it's wrong here
Model Monitor detects drift and quality degradation in deployed endpoints receiving live inference traffic, operating after training. It is tempting because it is a SageMaker bias-adjacent capability, but its bias monitoring runs against a deployed model's endpoint, not the pre-training dataset inside Data Wrangler.
- ✓
Built-in bias detection with SageMaker Clarify
Why this is correct
Data Wrangler's built-in bias detection invokes SageMaker Clarify to compute bias metrics such as class imbalance and disparate impact directly on the dataset before training. This satisfies the requirement to detect potential bias automatically during data preparation rather than post-training.
Go deeper
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 665-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.