MLS-C01 Exam Dumps - AWS Certified Machine Learning - Specialty

Amazon SageMaker Autopilot is a fully managed solution that automatically explores different ML models and selects the most effective ones for a given prediction task. After model training, Amazon SageMaker Clarify can generate feature importance scores, identifying the top features in a straightforward, automated manner with minimal manual intervention.

By using SageMaker Autopilot, the data scientist can obtain the desired feature importance ranking for predictive attributes with minimal setup and low operational overhead, as opposed to manually configuring models in SageMaker.

 To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images. This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.

References:

1: Image Augmentation - Amazon SageMaker

Amazon Kinesis Data Firehose is a service that can ingest streaming data and store it in various destinations, including Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. Amazon Kinesis Data Firehose can also convert the incoming data to Apache Parquet or Apache ORC format before storing it in Amazon S3. This can reduce the storage cost and improve the performance of analytical queries on the data. Amazon Kinesis Data Firehose supports various data sources, such as Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka, AWS IoT, and custom applications. Amazon Kinesis Data Firehose can also apply data transformation and compression using AWS Lambda functions.

AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or Parquet format.

Amazon Kinesis Data Streams is a service that can ingest and process streaming data in real time, but it does not store the data in any destination. Amazon Kinesis Data Streams can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format.

Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink, but it does not store the data in any destination. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format. References:

Amazon Kinesis Data Firehose - Amazon Web Services

What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose FAQs - Amazon Web Services

Amazon SageMaker Experiments is a service that helps track, compare, and evaluate different iterations of ML models. It can be used to capture key parameters such as the number of features and the sample count from a PySpark script that runs as a SageMaker processing job. A SageMaker processing job is a flexible and scalable way to run data processing workloads on AWS, such as feature engineering, data validation, model evaluation, and model interpretation.

References:

Amazon SageMaker Experiments

Process Data and Evaluate Models

 The issue described in the question is a sign of overfitting, which is a common problem in machine learning when the model learns the noise and details of the training data too well and fails to generalize to new and unseen data. Overfitting can result in a low training error rate but a high test error rate, which indicates poor performance and validity of the model. There are several techniques that can be used to prevent or reduce overfitting, such as data augmentation and regularization.

Data augmentation is a technique that applies various transformations to the original training data, such as rotation, scaling, cropping, flipping, adding noise, changing brightness, etc., to create new and diverse data samples. Data augmentation can increase the size and diversity of the training data, which can help the model learn more features and patterns and reduce the variance of the model. Data augmentation is especially useful for image data, as it can simulate different scenarios and perspectives that the model may encounter in real life. For example, in the question, the device uses a camera to observe drivers’ behavior, so data augmentation can help the model deal with different lighting conditions, angles, distances, etc. Data augmentation can be done using various libraries and frameworks, such as TensorFlow, PyTorch, Keras, OpenCV, etc12

Regularization is a technique that adds a penalty term to the model’s objective function, which is typically based on the model’s parameters. Regularization can reduce the complexity and flexibility of the model, which can prevent overfitting by avoiding learning the noise and details of the training data. Regularization can also improve the stability and robustness of the model, as it can reduce the sensitivity of the model to small fluctuations in the data. There are different types of regularization, such as L1, L2, dropout, etc., but they all have the same goal of reducing overfitting. L2 regularization, also known as weight decay or ridge regression, is one of the most common and effective regularization techniques. L2 regularization adds the squared norm of the model’s parameters multiplied by a regularization parameter (lambda) to the model’s objective function. L2 regularization can shrink the model’s parameters towards zero, which can reduce the variance of the model and improve the generalization ability of the model. L2 regularization can be implemented using various libraries and frameworks, such as TensorFlow, PyTorch, Keras, Scikit-learn, etc34

The other options are not valid or relevant for resolving the issue of overfitting. Adding vanishing gradient to the model is not a technique, but a problem that occurs when the gradient of the model’s objective function becomes very small and the model stops learning. Making the neural network architecture complex is not a solution, but a possible cause of overfitting, as a complex model can have more parameters and more flexibility to fit the training data too well. Using gradient checking in the model is not a technique, but a debugging method that verifies the correctness of the gradient computation in the model. Gradient checking is not related to overfitting, but to the implementation of the model.