MLA-C01 Amazon Web Services AWS Certified Machine Learning Engineer - Associate Free Practice Exam Questions (2026 Updated)

Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.

The correct answer is C. Class imbalance ratio.

Amazon SageMaker Clarify provides both pre-training and post-training bias detection capabilities to help data scientists ensure fairness in ML models. Pre-training bias metrics specifically analyze the training dataset to detect underrepresentation of certain groups or features before a model is trained. The class imbalance ratio measures how evenly different classes or sensitive attributes are represented in the dataset. A high imbalance indicates that a particular segment of customers (e.g., certain age groups, genders, or ethnicities) may be underrepresented, which could lead to biased predictions when the model is deployed.

Feature attribution bias (Option B) and prediction distribution skew (Option A) are post-training metrics. Feature attribution bias evaluates whether the model relies disproportionately on certain features for predictions, while prediction distribution skew examines whether the model’s outputs are skewed for specific groups. Model performance gap (Option D) is also a post-training measure, focusing on differences in accuracy or other performance metrics between groups.

By using the class imbalance ratio during pre-training analysis, the company can identify and mitigate underrepresentation before training the model. Actions may include resampling the dataset, weighting underrepresented classes, or applying synthetic data augmentation to ensure that the model is trained on a balanced, fair dataset. This aligns with AWS best practices for ML solution monitoring, maintenance, and security, particularly for responsible AI and fairness assessment.

In summary, the class imbalance ratio allows proactive bias detection at the dataset level, preventing unfair recommendations and ensuring equitable treatment across all customer segments in the recommendation system.

The problem described is a patient segmentation use case, which is a classic example of unsupervised learning. The objective is to group patients with similar characteristics without predefined labels. AWS documentation clearly states that Amazon SageMaker k-means is designed specifically for clustering and segmentation tasks.

The SageMaker k-means algorithm groups data points into clusters based on feature similarity and requires the user to define the number of clusters using the k hyperparameter. This directly satisfies the requirement to “determine the number of groups by using hyperparameters.” AWS recommends k-means for applications such as customer segmentation, risk grouping, and pattern discovery in healthcare data.

Option A (XGBoost) is a supervised learning algorithm used for classification and regression. The max_depth hyperparameter controls tree complexity, not the number of groups, making it unsuitable for this task.

Option C (DeepAR) is a time-series forecasting algorithm optimized for predicting future values, not clustering patients.

Option D (Random Cut Forest) is an anomaly detection algorithm. While useful for identifying outliers or unusual patient behavior, it does not perform clustering or group segmentation.

AWS SageMaker documentation explicitly identifies k-means as the correct choice when the goal is to partition data into a predefined number of clusters using a tunable hyperparameter.

Therefore, Option B is the correct and AWS-verified answer.

Amazon MSK Serverless provides a fully managed Apache Kafka-compatible service that automatically handles provisioning, scaling, and capacity management. AWS documentation states that MSK Serverless is designed for customers who want Kafka functionality without managing infrastructure.

Option B requires capacity planning and scaling management. Option C uses proprietary technology rather than open source. Option D requires full infrastructure management.

MSK Serverless delivers near real-time streaming with minimal operational overhead while maintaining compatibility with open-source Kafka tooling.

Therefore, Option A is the correct solution.

This problem describes a recommendation system with millions of records, many categorical variables, and a high-dimensional sparse feature space created by one-hot encoding. AWS documentation explicitly recommends Amazon SageMaker Factorization Machines (FM) for such use cases.

Factorization Machines are designed to handle sparse datasets efficiently and to model interactions between categorical features without explicitly enumerating all feature combinations. This capability makes FM particularly well-suited for recommendation problems such as predicting user-item interactions, including destination recommendations.

With 2,000 possible airport destinations, the target space is large and sparse. One-hot encoding further increases sparsity. Factorization Machines address this challenge by learning latent factors that capture relationships between features, even when many feature combinations are rarely observed.

Option A (CatBoost) is not an Amazon SageMaker built-in algorithm and therefore does not meet the requirement. Option B (DeepAR) is a time-series forecasting algorithm, not intended for recommendation or classification problems. Option D (k-means) is an unsupervised clustering algorithm and cannot directly predict a specific destination label.

AWS documentation explicitly lists recommendation systems and click prediction as primary use cases for the SageMaker Factorization Machines algorithm.

Therefore, Option C is the correct and AWS-verified choice.

SageMaker real-time inference is designed for low-latency, real-time use cases, such as detecting fraudulent transactions in banking applications. It eliminates the delays associated with SageMaker Asynchronous Inference, improving inference performance.

SageMaker Model Monitor provides tools to monitor deployed models for deviations in data quality, model performance, and other metrics. It can be configured to send notifications when a deviation in model quality is detected, ensuring the system remains reliable.

Explaining how a model makes predictions is the domain of model interpretability and explainability. Amazon SageMaker Clarify is designed specifically to provide explanations for ML predictions using techniques such as SHAP (SHapley Additive exPlanations).

SageMaker Clarify can analyze deployed endpoints to show feature importance, explain individual predictions, and quantify how each input feature contributes to the model’s output. This makes it ideal for communicating model behavior to non-technical stakeholders and meeting transparency requirements.

Model Monitor focuses on data and performance drift, not explanations. A/B testing and shadow endpoints compare performance but do not explain predictions.

Therefore, SageMaker Clarify is the correct solution for explaining model predictions.

The company lacks programming expertise, so a no-code/low-code solution is required. Amazon SageMaker Canvas includes Data Wrangler’s visual interface, which enables users to import, transform, and prepare data using a graphical workflow.

Data Wrangler in Canvas supports common preprocessing tasks—such as handling missing values, normalization, outlier detection, and feature engineering—without writing code. It integrates directly with Amazon S3 and is suitable for large tabular datasets.

Options A, C, and D require coding skills (Spark, SQL, or Python), which violates the stated constraint.

Therefore, using the visual interface of SageMaker Data Wrangler in Canvas is the correct solution.

In Amazon QuickSight anomaly detection, the Direction parameter controls which deviations from the expected baseline are flagged as anomalies. When Direction is set to All, QuickSight detects both higher-than-expected and lower-than-expected values. This results in a larger set of detected anomalies, increasing recall.

When the Direction parameter is changed to Lower than expected, the anomaly detection algorithm filters out all higher-than-expected anomalies and only reports unusually low values. Because the scope of detection is narrowed, the total number of detected anomalies decreases. As a result, fewer true anomalies are identified overall, which reduces recall.

Since fewer data points are evaluated as potential anomalies, the frequency of anomaly identification also decreases. This behavior is consistent with AWS documentation, which explains that directional filtering limits detection to a subset of deviations and therefore reduces overall anomaly counts.

Therefore, changing the Direction from All to Lower than expected leads to decreased anomaly identification frequency and decreased recall.

The primary goal is to produce the most complete dataset while handling missing values and extreme outliers appropriately. Dropping samples (Option A) or columns (Option D) would reduce data completeness and potentially remove valuable information, which contradicts the requirement.

Imputation is therefore the correct approach. Between mean and median imputation, AWS ML best practices recommend using the median when features contain outliers. The mean is sensitive to extreme values and can be skewed significantly, leading to imputed values that are not representative of the typical data distribution. In contrast, the median is robust to outliers, making it a better statistical estimator for central tendency in such datasets.

Amazon SageMaker Data Wrangler supports median imputation as a built-in transformation, enabling ML engineers to handle missing values consistently across large tabular datasets without custom code. This approach preserves all rows and columns while minimizing distortion caused by extreme values, which is particularly important for neural networks that are sensitive to input distributions.

Therefore, imputing missing values with the median value provides the most complete and statistically appropriate dataset for training.

The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.

AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.

Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.

Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.