Pass the Amazon Web Services MLA-C01 Questions and answers with ValidTests

For cross-account Amazon S3 access, AWS requires two components:

An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account

An IAM role policy in the consuming account (Account B) that allows the service to access the bucket

Amazon SageMaker training jobs assume an IAM role in the account where the job runs—in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.

The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.

Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.

AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.

Therefore, Option B is the correct and AWS-aligned solution.

Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no"). It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.

A sudden drop in model performance after a long period of stability is a classic indicator of data drift. According to AWS ML documentation, production data distribution drift occurs when the statistical properties of incoming data change over time compared to the training dataset.

This drift can be caused by changes in user behavior, market conditions, seasonal trends, or upstream data pipelines. When drift occurs, the model’s learned patterns no longer align with real-world data, leading to degraded accuracy and other performance metrics.

Lack of training data (Option A) and overfitting (Option D) are issues that typically manifest during initial training, not after months of stable production performance. Compute constraints (Option C) may affect latency but do not usually cause sustained accuracy degradation.

AWS recommends using SageMaker Model Monitor to detect such drift and trigger retraining workflows.

Therefore, drift in the production data distribution is the most likely cause of the observed performance degradation.

Different data types require different encoding strategies. The Feedback column contains long, unstructured text responses. According to AWS ML documentation, text data must first be converted into tokens before it can be vectorized using techniques such as embeddings or bag-of-words. Tokenization is the correct preprocessing step for textual features.

The Satisfaction Level column is categorical but has a natural ordering (Low < Medium < High). AWS best practices recommend ordinal encoding for such ordered categorical variables because it preserves the inherent ranking information.

Option A is incorrect because one-hot encoding is not suitable for free-form text and would create an unmanageable number of features. Option B has the same issue for the Feedback column. Option C incorrectly applies label encoding to text and binary encoding to a three-class ordinal variable.

Therefore, tokenization for text data and ordinal encoding for satisfaction levels is the correct solution.

Problem Description:

The F1 score, which is a balance of precision and recall, has decreased significantly. This indicates the model's predictions are no longer aligned with the real-world data distribution.

Why Concept Drift?

Concept drift occurs when the statistical properties of the target variable or features change over time. For example, customer behaviors or subscription cancellation patterns may have shifted, leading to reduced model accuracy.

Signs of Concept Drift:

Deviation in performance metrics (e.g., F1 score) over time.

Declining prediction accuracy for certain groups or scenarios.

Solution:

Monitor for drift using tools like SageMaker Model Monitor.

Regularly retrain the model with updated data to account for the drift.

Why Not Other Options?:

B: Model complexity is unrelated if the model initially performed well.

C: Data quality issues would have been detected during baseline analysis.

D: Incorrect ground truth labels would have resulted in a consistently poor baseline.

Conclusion: The decrease in F1 score is most likely due to concept drift in the customer data, requiring retraining of the model with new data.

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.

Callback steps in Amazon SageMaker Pipelines allow you to integrate external processes, such as AWS Glue jobs, into the pipeline workflow. By using a Callback step, the SageMaker pipeline can trigger the AWS Glue workflow and pause execution until the Glue jobs complete. This approach provides seamless integration with minimal operational overhead, as it directly ties the pipeline's execution flow to the completion of the AWS Glue jobs without requiring additional orchestration tools or complex setups.

AWS Compute Optimizer analyzes the resource usage of Amazon EC2 instances, ECS services, Lambda functions, and Amazon EBS volumes. It provides actionable recommendations to optimize resource utilization and reduce costs, such as resizing instances, moving workloads to Spot Instances, or changing volume types. This solution requires the least development effort because Compute Optimizer is a managed service that automatically generates insights and recommendations based on historical usage data.

The ETL process has two critical characteristics: it is long-running (6 hours) and written in R. AWS Lambda is unsuitable because it has a maximum execution time of 15 minutes. AWS Glue primarily supports Spark-based ETL and does not natively support custom R-based workloads.

AWS documentation recommends using Amazon SageMaker Processing Jobs for long-running, custom data processing workloads. Processing jobs allow users to run arbitrary code in custom Docker containers, making them ideal for migrating on-premises ETL jobs written in R.

By building a custom Docker image that includes the R runtime and required libraries and storing it in Amazon ECR, the company can run the ETL job at scale on managed infrastructure without rewriting the code.

SageMaker script mode is intended for training, not ETL. Therefore, SageMaker processing jobs with a custom container are the correct solution.

AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.

AWS documentation clearly specifies the required order:

Create a baseline using training data statistics

Create a monitoring schedule to compare incoming data against the baseline

Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.

Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.

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