- A
Only the inference data with predictions
Why wrong: Without baseline data, Clarify cannot compute relative bias drift.
- B
Only the ground truth labels for recent predictions
Why wrong: Ground truth alone is insufficient; baseline distributions are needed.
- C
Only the training data with feature attributions
Why wrong: Bias monitoring requires both training and inference data for drift comparison.
- D
Baseline training data with ground truth labels and inference data with predictions
Clarify bias monitoring requires a baseline dataset (training data with labels) and current inference data (with predictions and ground truth when available) to compute bias metrics over time.
Question 903 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 financial services company is deploying a fraud detection model on SageMaker. To comply with regulations, they must ensure that the model's predictions are not biased against protected groups. They plan to monitor bias drift post-deployment using SageMaker Clarify. Which data inputs are required to configure Clarify's bias drift monitoring?
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
✓
Baseline training data with ground truth labels and inference data with predictions
Option D is correct because SageMaker Clarify's bias drift monitoring requires a baseline—specifically, the training data with ground truth labels—to establish the original bias metrics, and the inference data with predictions to compute post-deployment bias metrics. By comparing these two datasets, Clarify detects statistically significant shifts in bias over time, which is essential for regulatory compliance in fraud detection models.
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.
- ✗
Only the inference data with predictions
Why it's wrong here
Without baseline data, Clarify cannot compute relative bias drift.
- ✗
Only the ground truth labels for recent predictions
Why it's wrong here
Ground truth alone is insufficient; baseline distributions are needed.
- ✗
Only the training data with feature attributions
Why it's wrong here
Bias monitoring requires both training and inference data for drift comparison.
- ✓
Baseline training data with ground truth labels and inference data with predictions
Why this is correct
Clarify bias monitoring requires a baseline dataset (training data with labels) and current inference data (with predictions and ground truth when available) to compute bias metrics over time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume only inference data is needed for monitoring, overlooking the critical requirement of a baseline training dataset with ground truth labels to measure drift against.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Clarify uses statistical tests like the Equal Opportunity Difference or Demographic Parity Difference to quantify bias. For drift monitoring, it compares the bias metrics computed on the baseline training data (with labels) against those on the inference data (with predictions), using a configurable threshold (e.g., 0.1) to trigger alerts. In a real-world scenario, if a fraud model becomes biased against a protected group due to data drift, Clarify's monitoring can detect this shift before regulatory audits.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Baseline training data with ground truth labels and inference data with predictions — Option D is correct because SageMaker Clarify's bias drift monitoring requires a baseline—specifically, the training data with ground truth labels—to establish the original bias metrics, and the inference data with predictions to compute post-deployment bias metrics. By comparing these two datasets, Clarify detects statistically significant shifts in bias over time, which is essential for regulatory compliance in fraud detection models.
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.
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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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