hardMultiple Choice
MLA-C01 Practice Question: A financial services company operates a real-time…
A financial services company operates a real-time inference endpoint for a fraud detection model on Amazon SageMaker. The model was trained on historical transaction data from 2023. Over the past month, the model's precision has dropped from 92% to 78%, while recall remains high at 95%. The data science team suspects data drift and has already enabled SageMaker Model Monitor with data capture and a baseline from the training data. The latest monitoring report indicates no statistically significant drift in any of the input features. The team also verified that the inference code and model artifact have not changed. Despite the stable feature distributions, the model is misclassifying an increasing number of legitimate transactions as fraudulent (false positives). The business is concerned about the impact on customer experience. What is the best course of action?
⚠ Common exam trap
MLA-C01 often tests the distinction between feature drift and label drift, so candidates must remember that stable feature distributions do not rule out label drift, which requires investigating ground truth labels.
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
✓
Investigate recent ground truth labels to check for label drift or changes in the fraud definition.
The model's precision dropped while recall remained high, meaning it is flagging more legitimate transactions as fraudulent (false positives). Since input feature distributions show no drift and the model artifact is unchanged, the issue likely lies in the target variable—label drift or a change in the definition of fraud. Investigating recent ground truth labels will reveal if the labels used for evaluation or retraining have shifted, which would explain the degradation. This is the best course of action before retraining or changing the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the model with a more complex algorithm such as a gradient-boosted tree.
Why it's wrong here
Algorithm change is a premature solution; the model's performance drop is likely due to data changes, not model capacity.
- ✗
Retrain the model using the most recent 30 days of transaction data with automated retraining pipelines.
Why it's wrong here
Retraining without understanding the cause may still use incorrect labels or miss a shift in the label distribution; it's better to first investigate ground truth.
- ✗
Increase the data capture sampling percentage from 10% to 100% for more detailed analysis.
Why it's wrong here
Increasing data capture will provide more data but does not address the need for ground truth labels to diagnose the degradation.
- ✓
Investigate recent ground truth labels to check for label drift or changes in the fraud definition.
Why this is correct
Label drift occurs when the underlying relationship between features and labels changes. Collecting and analyzing recent labels can confirm if the fraud criteria have shifted.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.