MLA-C01 Online Practice Questions

Explanation:
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.

Explanation:
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.

Explanation:
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.

Explanation:
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.

Explanation:
One-hot encoding is the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.