Amazon Web Services MLA-C01 Exam Dumps

D.   Create a data flow in SageMaker Data Wrangler. Configure a transform step.

Answer Description

Explanation:

SageMaker Data Wrangler provides a no-code/low-code interface for preparing and transforming data, including dropping unnecessary columns. By creating a data flow and configuring a transform step, the ML engineer can easily remove correlated or unneeded columns from the Parquet file with minimal effort. This approach avoids the need for custom coding or managing additional infrastructure.

D.   Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

A.   Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

Answer Description

Explanation:

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.

C.   Logistic regression

Answer Description

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.

A.   Configure the competitor's name as a blocked phrase in Amazon Q Business.

Answer Description

Explanation:

Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

A.   Enable early stopping on the model.


B.   Increase dropout in the layers.

Answer Description

Explanation:

Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.
Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.

A.   AWS::SageMaker::Model

Answer Description

Explanation:

The AWS::SageMaker::Model resource in AWS CloudFormation is used to create an ML model in Amazon SageMaker. This model can then be hosted on an endpoint by using the AWS::SageMaker::Endpoint resource. The model resource defines the container or algorithm to use for hosting and the S3 location of the model artifacts.

D.   Mean absolute error (MAE)

Answer Description

Explanation:

When predicting continuous variables, such as apartment prices, it's essential to evaluate the model's performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.

Understanding Mean Absolute Error (MAE):
MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.
Advantages of MAE:
Interpretability:MAE is expressed in the same units as the target variable, making it easy to understand.
Robustness to Outliers:Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.
Comparison with Other Metrics:
Accuracy, AUC, F1 Score:These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.
Mean Squared Error (MSE):While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.

Conclusion:
For evaluating the performance of a model predicting apartment prices—a continuous variable—MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.