Amazon Web Services Updated MLA-C01 Exam Questions and Answers by lowri

Amazon EMR clusters consist of primary, core, and task nodes, each with a distinct role. The primary node manages the cluster, core nodes store data and run tasks, and task nodes only run tasks without storing data. AWS documentation recommends using task nodes for scaling compute capacity when workloads are compute-intensive, such as data ingestion and transformation pipelines.

To reduce processing time cost-effectively, AWS strongly advises using Spot Instances for task nodes. Spot Instances provide the same compute capacity as On-Demand Instances but at a significantly reduced cost, often up to 90% lower. Because task nodes do not store HDFS data, they can be safely interrupted without risking data loss.

Increasing the number of primary nodes is not supported by EMR and would not improve performance. Increasing core nodes affects both storage and compute and is more expensive, especially when using On-Demand Instances. Option D is therefore the least cost-effective.

AWS EMR best practices explicitly state that scaling out with Spot task nodes is the preferred way to improve performance for transient, parallel workloads such as ETL, ingestion, and feature preparation.

Therefore, Option C is the most cost-effective and AWS-recommended solution.

The text feature contains high-cardinality categorical values with minor spelling variations, which is a common real-world data quality issue. Traditional encoding techniques such as ordinal encoding (Option A) or one-hot encoding (Option C) would treat each misspelled value as a completely separate category, resulting in poor generalization and very high dimensionality.

Similarity encoding addresses this problem by grouping or encoding categories based on string similarity. Categories that differ only slightly—such as spelling errors—are encoded with similar numerical representations. Amazon SageMaker Data Wrangler supports similarity encoding specifically for such noisy categorical features.

Target encoding (Option D) depends on the target label distribution and risks target leakage if not carefully applied.

Therefore, similarity encoding is the most appropriate and AWS-recommended solution.

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.

 Problem Description:

The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model’s ability to learn patterns from the minority class.

 Why SageMaker Data Wrangler?

SageMaker Data Wrangler provides a built-in operation called " Balance Data, " which includes oversampling and undersampling techniques to address class imbalances.

Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.

 Steps to Implement:

Import the dataset into SageMaker Data Wrangler.

Apply the " Balance Data " operation and configure it to oversample the minority class.

Export the balanced dataset for training.

 Advantages:

Ease of Use: Minimal configuration is required.

Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.

Time Efficiency: Reduces manual effort compared to external tools or scripts.