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Amazon MLA-C01 Exam Questions
Free Amazon MLA-C01 Exam Actual Questions
Note: Premium Questions for MLA-C01 were last updated On May. 17, 2026 (see below)
A company wants to improve its customer retention ML model. The current model has 85% accuracy and a new model shows 87% accuracy in testing. The company wants to validate the new model's performance in production.
Which solution will meet these requirements?
AWS ML best practices recommend A/B testing to validate model improvements in production while minimizing risk. By routing a controlled portion of live traffic (for example, 20%) to the new model and keeping the majority of traffic on the existing model, the company can directly compare real-world performance using the same data distribution.
This approach allows statistically meaningful comparison of business metrics such as customer retention, rather than relying solely on offline accuracy. It also limits potential negative impact if the new model underperforms in production.
Deploying the new model to 100% of traffic (Option A) introduces unnecessary risk. Offline analysis (Option C) does not reflect live user behavior. Alternating deployments (Option D) introduces confounding factors such as time-based effects.
Therefore, A/B testing is the correct solution.
An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.
The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.
What should the ML engineer do to transform the data and make the data suitable for training?
This dataset shows severe class imbalance, with only 2% of records representing patients with the disease. AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.
Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model's ability to learn disease-related patterns without discarding data.
PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.
Therefore, applying SMOTE to the training dataset is the correct approach.
An ML engineer is setting up a CI/CD pipeline for an ML workflow in Amazon SageMaker AI. The pipeline must automatically retrain, test, and deploy a model whenever new data is uploaded to an Amazon S3 bucket. New data files are approximately 10 GB in size. The ML engineer also needs to track model versions for auditing.
Which solution will meet these requirements?
AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.
For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.
AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.
AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.
Therefore, Option B is the correct and AWS-verified solution.
An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The model must make continuous numeric predictions based on 10 years of data.
Which metric should the ML engineer use to evaluate the model's performance?
This is a regression problem, where the target variable is continuous and numeric. AWS documentation clearly states that classification metrics such as accuracy and AUC are not appropriate for regression models.
Root Mean Square Error (RMSE) measures the square root of the average squared differences between predicted and actual values. RMSE penalizes larger errors more heavily, making it especially useful when large prediction errors are costly or undesirable.
SageMaker Canvas automatically selects regression metrics such as RMSE and MAE when building regression models. RMSE is widely used for time-based and numeric prediction problems, especially when evaluating long historical datasets.
Inference latency measures system performance, not model accuracy.
Therefore, Option D is the correct and AWS-verified answer.
An ML engineer is setting up a CI/CD pipeline for an ML workflow in Amazon SageMaker AI. The pipeline must automatically retrain, test, and deploy a model whenever new data is uploaded to an Amazon S3 bucket. New data files are approximately 10 GB in size. The ML engineer also needs to track model versions for auditing.
Which solution will meet these requirements?
AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.
For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.
AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.
AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.
Therefore, Option B is the correct and AWS-verified solution.
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