MLA-C01 Exam Dumps : AWS Certified Machine Learning Engineer - Associate

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.

Logistic regression requires numeric input features and is sensitive to how categorical variables are encoded. AWS feature engineering best practices recommend one-hot encoding for low-cardinality categorical variables with no inherent order and ordinal encoding for categorical variables with a meaningful order.

The location feature has only three distinct values and no ordinal relationship, making one-hot encoding the most appropriate method. This prevents the model from inferring a false numerical relationship between locations.

The job_seniority_level feature typically has an inherent order (for example: junior, mid-level, senior, lead). Even with more than 10 categories, ordinal encoding preserves this natural hierarchy while keeping the feature dimensionality manageable.

Tokenization is used for unstructured text, not structured categorical variables. Standard scaling applies only to continuous numeric features and is not suitable for categorical string variables.

AWS documentation explicitly highlights using one-hot encoding for nominal features and ordinal encoding for ordered categorical features when preparing data for linear models such as logistic regression.

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

Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.

AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.

This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.