MLA-C01 Exam Results

The requirement specifies real-time streaming ingestion and column-level transformation before sharing data with a vendor. Amazon Kinesis Data Streams is designed for low-latency, real-time data ingestion and delivery.

To extract only required columns from the stream, AWS recommends using Amazon Managed Service for Apache Flink as a stream consumer. Flink enables real-time transformations such as filtering, projection, and enrichment on streaming data before delivering it downstream.

Option A and D are batch-oriented and not suitable for real-time streaming. Option C is incorrect because S3 bucket policies cannot enforce column-level access controls.

Therefore, Kinesis Data Streams combined with Apache Flink meets all requirements.

Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.

AWS ML best practices recommend removing identifiers that are not causally related to the target variable. Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.

Therefore, Option A is the correct and AWS-aligned choice.

Amazon SageMaker automatically tracks the Invocations metric, which represents the number of API calls made to the endpoint, in Amazon CloudWatch. By adding this metric to a CloudWatch dashboard, you can monitor the endpoint's activity in real-time. Setting up a CloudWatch alarm allows the system to send notifications whenever the API call events exceed the defined threshold, meeting both the monitoring and notification requirements efficiently.

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.