MLS-C01 Amazon Web Services Exam Lab Questions

 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.

The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.

The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.

References:

Bias Detection and Model Explainability - Amazon SageMaker Clarify - AWS

Amazon SageMaker Clarify Model Explainability

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability

GitHub - aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning

 The plot in the graphic shows a right-skewed distribution, which violates the assumption of normality for linear regression. To correct this, the Data Scientist should apply a logarithmic transformation to the feature. This will help to make the distribution more symmetric and closer to a normal distribution, which is a key assumption for linear regression. References:

Linear Regression

Linear Regression with Amazon Machine Learning

Machine Learning on AWS

 To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:

Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.

Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.

The other options are not valid because:

Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.

Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.

Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:

Amazon Translate

Amazon Comprehend

Amazon SageMaker

Amazon SageMaker Neural Topic Model (NTM) Algorithm

Amazon SageMaker BlazingText

Amazon SageMaker Seq2Seq

Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm

Amazon Lex