Get MLS-C01 Dumps : Verified AWS Certified Machine Learning - Specialty

 AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234

The other options are not valid or secure ways to encrypt the data and protect the credit card information. Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

A naive Bayesian model assumes that the features are conditionally independent given the class label. This means that the joint probability of the features and the class can be factorized as the product of the class prior and the feature likelihoods. A full Bayesian network, on the other hand, does not make this assumption and allows for modeling arbitrary dependencies between the features and the class using a directed acyclic graph. In this case, the joint probability of the features and the class is given by the product of the conditional probabilities of each node given its parents in the graph. If the features are statistically dependent, meaning that their correlation coefficients are not close to zero, then a naive Bayesian model would not capture these dependencies and would likely perform worse than a full Bayesian network that can account for them. Therefore, a full Bayesian network describes the underlying data better in this situation. References:

• Naive Bayes and Text Classification I
• Bayesian Networks

 Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company’s problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.

References:

• Build high performing image classification models using Amazon SageMaker JumpStart
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning
• Data augmentation for improving deep learning in image classification problem
• Class-Adaptive Data Augmentation for Image Classification

The best solution to meet the requirements is to apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training. SMOTE is a technique that generates synthetic samples for the minority class by interpolating between existing samples. This can help balance the class distribution and provide more information to the model. SMOTE can improve the performance of the model on the minority class, which is the class of interest in churn prediction. SMOTE can be applied using the SageMaker Data Wrangler, which provides a built-in analysis for oversampling the minority class1.

The other options are not effective solutions for the problem. Applying anomaly detection to remove outliers from the training dataset before training may not improve the model’s accuracy, as outliers may not be the main cause of the false results. Moreover, removing outliers may reduce the diversity of the data and make the model less robust. Applying normalization to the features of the training dataset before training may improve the model’s convergence and stability, but it does not address the class imbalance issue. Normalization can also be applied using the SageMaker Data Wrangler, which provides a built-in transformation for scaling the features2. Applying undersampling to the training dataset before training may reduce the class imbalance, but it also discards potentially useful information from the majority class. Undersampling can also result in underfitting and high bias for the model.

References:

•Analyze and Visualize

•Transform and Export

•SMOTE for Imbalanced Classification with Python

•Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

The most secure action to protect the data from being accessed and transferred to a remote host by malicious code accidentally installed on the training container is to enable network isolation for training jobs. Network isolation is a feature that allows you to run training and inference containers in internet-free mode, which blocks any outbound network calls from the containers, even to other AWS services such as Amazon S3. Additionally, no AWS credentials are made available to the container runtime environment. This way, you can prevent unauthorized access to your data and resources by malicious code or users. You can enable network isolation by setting the EnableNetworkIsolation parameter to True when you call CreateTrainingJob, CreateHyperParameterTuningJob, or CreateModel.

References:

• Run Training and Inference Containers in Internet-Free Mode - Amazon SageMaker