MLS-C01 Exam Dumps - Amazon Web Services AWS Certified Specialty Questions and Answers

To leverage the NVIDIA GPUs on Amazon EC2 P3 instances, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker. It automatically configures the container to access the NVIDIA drivers and libraries on the host system. The Specialist does not need to bundle the NVIDIA drivers with the Docker image, as they are already installed on the EC2 P3 instances. The Specialist does not need to organize the Docker container’s file structure to execute on GPU instances, as this is not relevant for GPU compatibility. The Specialist does not need to set the GPU flag in the Amazon SageMaker Create TrainingJob request body, as this is only required for using Elastic Inference accelerators, not EC2 P3 instances. References: NVIDIA-Docker, Using GPU-Accelerated Containers, Using Elastic Inference in Amazon SageMaker

The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. This solution has the following advantages:

It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1.

It does not require the claim contents or any other features to make predictions, which simplifies the data preparation and reduces the impact of missing or incomplete data2.

It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.

It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.

The other solutions have the following drawbacks:

A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the temporal aspect of the data, which is important for forecasting1.

B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.

D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to handle multiple outputs, and the ignorance of temporal patterns123.

1: Time Series Forecasting - Amazon SageMaker

2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog

3: Forecasting vs Classification: What’s the Difference? | DataRobot

4: Amazon Forecast – Time Series Forecasting Made Easy | AWS News Blog

5: Reinforcement Learning - Amazon SageMaker

6: What is Reinforcement Learning? The Complete Guide | Edureka

7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science

 The solution A will meet the requirements with the most operational efficiency because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution A involves the following steps:

Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Amazon SageMaker Data Wrangler provides a visual interface that allows data scientists to apply various transformations to their tabular data, such as encoding categorical features, scaling numerical features, imputing missing values, and more. Amazon SageMaker Data Wrangler also supports custom transformations using Python code or SQL queries1.

Use SageMaker Data Wrangler templates for visualization. Amazon SageMaker Data Wrangler also provides a set of templates that can generate visualizations of the data, such as histograms, scatter plots, box plots, and more. These visualizations can help data scientists to understand the distribution and characteristics of the data, and to compare the effects of different feature transformations1.

Export the feature processing workflow to a SageMaker pipeline for automation. Amazon SageMaker Data Wrangler can export the feature processing workflow as a SageMaker pipeline, which is a service that orchestrates and automates machine learning workflows. A SageMaker pipeline can run the feature processing steps as a preprocessing step, and then feed the output to a training step or an inference step. This can reduce the operational overhead of managing the feature processing workflow and ensure its consistency and reproducibility2.

The other options are not suitable because:

Option B: Using an Amazon SageMaker notebook instance to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to write the code for the feature transformations, the data storage, the data visualization, and the Lambda function. Moreover, AWS Lambda has limitations on the execution time, memory size, and package size, which may not be sufficient for complex feature processing tasks3.

Option C: Using AWS Glue Studio with custom code to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. AWS Glue Studio is a visual interface that allows data engineers to create and run extract, transform, and load (ETL) jobs on AWS Glue. However, AWS Glue Studio does not provide preconfigured transformations or templates for feature engineering or data visualization. The data scientist will have to write custom code for these tasks, as well as for the Lambda function. Moreover, AWS Glue Studio is not integrated with SageMaker pipelines, and it may not be optimized for machine learning workflows4.

Option D: Using Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, packaging each feature transformation step into a separate AWS Lambda function, and using AWS Step Functions for workflow automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to create and manage multiple AWS Lambda functions and AWS Step Functions, which can increase the complexity and cost of the solution. Moreover, AWS Lambda and AWS Step Functions may not be compatible with SageMaker pipelines, and they may not be optimized for machine learning workflows5.

1: Amazon SageMaker Data Wrangler

2: Amazon SageMaker Pipelines

3: AWS Lambda

4: AWS Glue Studio

5: AWS Step Functions

 Early stopping is a technique that can be used to prevent overfitting and improve model performance on the validation set. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the graph, where the training loss keeps decreasing, but the validation loss starts to increase after some point. This means that the model is fitting the noise and patterns in the training data that are not relevant for the validation data. Early stopping is a way of stopping the training process before the model overfits the training data. It works by monitoring the validation loss and stopping the training when the validation loss stops decreasing or starts increasing. This way, the model is saved at the point where it has the best performance on the validation set. Early stopping can also save time and resources by reducing the number of epochs needed for training. References:

Early Stopping

How to Stop Training Deep Neural Networks At the Right Time Using Early Stopping

To perform efficient exploratory data analysis (EDA) on a large dataset for anomaly detection, using the First K option in SageMaker Data Wrangler is an optimal choice. This option allows the data scientist to select the first K rows, limiting the data loaded into memory, which conserves compute resources.

Given that the First K option allows the data scientist to determine K based on domain knowledge, this approach provides a representative sample without requiring extensive compute resources. Other options like randomized sampling may not provide data samples that are as useful for initial analysis in a time-series or sequential dataset context.

 The best model for predicting housing prices based on a historical dataset with 32 features is linear regression. Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Regression vs Classification in Machine Learning

AWS Machine Learning Training - Linear Regression with Amazon SageMaker

To comply with the company’s security requirement that SageMaker notebook instances must not have internet connectivity, the most secure and recommended approach is to use VPC interface endpoints (AWS PrivateLink) for connecting SageMaker to other AWS services such as Amazon S3, without requiring internet access or a NAT Gateway.

Exact Extract from Amazon SageMaker documentation:

“You can connect to your notebook instance from your VPC through an interface endpoint in your Virtual Private Cloud (VPC) instead of connecting over the public internet. When you use a VPC interface endpoint, communication between your VPC and the notebook instance is conducted entirely and securely within the AWS network.”

Further security can be enforced using IAM and security group restrictions:

“To restrict access to only connections made from within your VPC, create an AWS Identity and Access Management (IAM) policy that restricts access to only calls that come from within your VPC. Then add that policy to every AWS Identity and Access Management user, group, or role used to access the notebook instance.”

Also:

“Use VPC endpoints to keep traffic between your VPC and SageMaker in the AWS network. This is especially useful when your notebook instances or endpoints don’t have internet access.”

By configuring interface VPC endpoints and controlling security groups and IAM policies appropriately, the SageMaker notebook instances can operate without any internet connectivity, meeting the company’s policy securely and effectively.

Amazon SageMaker’s Neural Topic Model (NTM) is designed to uncover underlying topics within text data by clustering documents based on topic similarity. For document categorization, NTM can identify product categories by analyzing and grouping the documents, making it an efficient choice for unsupervised learning where predefined categories do not exist.

This model minimizes operational overhead by allowing automated topic categorization directly within SageMaker, which is more straightforward than custom Docker image setups or using k-means clustering for tokenized data.

Area Under the ROC Curve (AUC) is a metric that is best suited to score the model for the given scenario. AUC is a measure of the performance of a binary classifier, such as a model that predicts whether a credit card transaction is valid or fraudulent. AUC is calculated based on the Receiver Operating Characteristic (ROC) curve, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of the classifier as the decision threshold is varied. The TPR, also known as recall or sensitivity, is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. The FPR, also known as the fall-out, is the proportion of actual negative cases (valid transactions) that are incorrectly predicted as positive by the classifier. The ROC curve illustrates how well the classifier can distinguish between the two classes, regardless of the class distribution or the error costs. A perfect classifier would have a TPR of 1 and an FPR of 0 for all thresholds, resulting in a ROC curve that goes from the bottom left to the top left and then to the top right of the plot. A random classifier would have a TPR and an FPR that are equal for all thresholds, resulting in a ROC curve that goes from the bottom left to the top right of the plot along the diagonal line. AUC is the area under the ROC curve, and it ranges from 0 to 1. A higher AUC indicates a better classifier, as it means that the classifier has a higher TPR and a lower FPR for all thresholds. AUC is a useful metric for imbalanced classification problems, such as the credit card transaction dataset, because it is insensitive to the class imbalance and the error costs. AUC can capture the overall performance of the classifier across all possible scenarios, and it can be used to compare different classifiers based on their ROC curves.

The other options are not as suitable as AUC for the given scenario for the following reasons:

Precision: Precision is the proportion of predicted positive cases (fraudulent transactions) that are actually positive. Precision is a useful metric when the cost of a false positive is high, such as in spam detection or medical diagnosis. However, precision is not a good metric for imbalanced classification problems, because it can be misleadingly high when the positive class is rare. For example, a classifier that predicts all transactions as valid would have a precision of 0, but a very high accuracy of 99%. Precision is also dependent on the decision threshold and the error costs, which may vary for different scenarios.

Recall: Recall is the same as the TPR, and it is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. Recall is a useful metric when the cost of a false negative is high, such as in fraud detection or cancer diagnosis. However, recall is not a good metric for imbalanced classification problems, because it can be misleadingly low when the positive class is rare. For example, a classifier that predicts all transactions as fraudulent would have a recall of 1, but a very low accuracy of 1%. Recall is also dependent on the decision threshold and the error costs, which may vary for different scenarios.

Root Mean Square Error (RMSE): RMSE is a metric that measures the average difference between the predicted and the actual values. RMSE is a useful metric for regression problems, where the goal is to predict a continuous value, such as the price of a house or the temperature of a city. However, RMSE is not a good metric for classification problems, where the goal is to predict a discrete value, such as the class label of a transaction. RMSE is not meaningful for classification problems, because it does not capture the accuracy or the error costs of the predictions.

ROC Curve and AUC

How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python

Precision-Recall

Root Mean Squared Error

When an Amazon SageMaker notebook instance needs to access encrypted data in Amazon S3, the ML specialist must ensure that both Amazon S3 access permissions and AWS Key Management Service (KMS) decryption permissions are properly configured. The dataset in this scenario is stored with server-side encryption using an AWS KMS key (SSE-KMS), so the following steps are necessary:

S3 Read Permissions: Attach an IAM role to the SageMaker notebook instance with permissions that allow the s3:GetObject action for the specific S3 bucket storing the data. This will allow the notebook instance to read data from Amazon S3.

KMS Key Policy Permissions: Grant permissions in the KMS key policy to the IAM role assigned to the SageMaker notebook instance. This allows SageMaker to use the KMS key to decrypt data in the S3 bucket.

These steps ensure the SageMaker notebook instance can access the encrypted data stored in S3. The AWS documentation emphasizes that to access SSE-KMS encrypted data, the SageMaker notebook requires appropriate permissions in both the S3 bucket policy and the KMS key policy, making Option C the correct and secure approach.