Amazon Web Services MLS-C01 - AWS Certified Machine Learning - Specialty

To build a robust serverless data lake on Amazon S3 that meets the requirements, the financial services company should use the following AWS services:

AWS Glue crawler: This is a service that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for the data, and then creates metadata tables in the AWS Glue Data Catalog1. The company can use an AWS Glue crawler to crawl the S3 data and infer the schema, format, and partition structure of the data. The crawler can also detect schema changes and update the metadata tables accordingly. This enables the company to support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum, which are serverless interactive query services that use the AWS Glue Data Catalog as a central location for storing and retrieving table metadata23.

AWS Lambda function: This is a service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume - there is no charge when your code is not running. You can also use AWS Lambda to create event-driven ETL pipelines, by triggering other AWS services based on events such as object creation or deletion in S3 buckets4. The company can use an AWS Lambda function to trigger an AWS Glue ETL job, which is a serverless way to extract, transform, and load data for analytics. The AWS Glue ETL job can perform various data processing tasks, such as converting data formats, filtering, aggregating, joining, and more.

AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos, and use that metadata to query and transform the data. The company can use the AWS Glue Data Catalog to search and discover metadata, such as table definitions, schemas, and partitions. The AWS Glue Data Catalog also integrates with Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and AWS Glue ETL jobs, providing a consistent view of the data across different query and analysis services.

1: What Is a Crawler? - AWS Glue

2: What Is Amazon Athena? - Amazon Athena

3: Amazon Redshift Spectrum - Amazon Redshift

4: What is AWS Lambda? - AWS Lambda

AWS Glue ETL Jobs - AWS Glue

What Is the AWS Glue Data Catalog? - AWS Glue

The best approach to identify and visualize the natural groupings for the numerical columns across all customers is to embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot. t-SNE is a dimensionality reduction technique that can project high-dimensional data into a lower-dimensional space, while preserving the local structure and distances of the data points. A scatter plot can then show the clusters of data points in the reduced space, where each point represents a customer and the color indicates the cluster membership. This approach can help the Specialist quickly explore the patterns and similarities among the customers based on their numerical features.

The other options are not as effective or efficient as the t-SNE approach. Running k-means for different values of k and creating an elbow plot can help determine the optimal number of clusters, but it does not provide a visual representation of the clusters or the customers. Embedding the numerical features using t-SNE and creating a line graph does not make sense, as a line graph is used to show the change of a variable over time, not the distribution of data points in a space. Running k-means for different values of k and creating box plots for each numerical column within each cluster can provide some insights into the statistics of each cluster, but it is very time-consuming and cumbersome to create and compare thousands of box plots. References:

Dimensionality Reduction - Amazon SageMaker

Visualize high dimensional data using t-SNE - Amazon SageMaker

The company needs to transcribe petabytes of audio files from an on-premises VoIP system to an S3 bucket in the AWS Cloud. The transcription algorithm requires GPUs for inference, which are not available on the on-premises system. The VPN connection over a 100 Mbps connection is not sufficient to transfer the large amount of data quickly. Therefore, the company should use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm locally and leverage the GPU power. The device can store up to 42 TB of data and can be shipped back to AWS for data ingestion. The company can use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket in the AWS Cloud. This solution minimizes the network bandwidth and latency issues and enables faster data processing and transfer.

Option B is incorrect because AWS Snowcone is a small, portable, rugged, and secure edge computing and data transfer device that can store up to 8 TB of data. It is not suitable for processing petabytes of data and does not support GPU-based instances.

Option C is incorrect because AWS Outposts is a service that extends AWS infrastructure, services, APIs, and tools to virtually any data center, co-location space, or on-premises facility. It is not designed for data transfer and ingestion, and it would require additional infrastructure and maintenance costs.

Option D is incorrect because AWS DataSync is a service that makes it easy to move large amounts of data to and from AWS over the internet or AWS Direct Connect. However, using DataSync to ingest the audio files to S3 would still be limited by the network bandwidth and latency. Moreover, running the transcription algorithm on AWS Lambda would incur additional costs and complexity, and it would not leverage the GPU power that the algorithm requires.

AWS Snowball Edge Compute Optimized

AWS DataSync

AWS Snowcone

AWS Outposts

AWS Lambda

The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1

The other options are incorrect because they either do not update the software or require unnecessary steps. For example:

Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2

Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3

Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4

1: Amazon SageMaker Notebook Instances - Amazon SageMaker

2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker

3: Create a Notebook Instance - Amazon SageMaker

4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker

The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.

pandas.DataFrame.groupby — pandas 2.1.4 documentation

pandas.DataFrame.plot.bar — pandas 2.1.4 documentation

Matplotlib - Bar Plot - Online Tutorials Library

The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters. References:

Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.

K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

 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.

1: Image Augmentation - Amazon SageMaker

Amazon SageMaker Studio Data Wrangler is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using Data Wrangler, the data scientist can easily import the time-series data from various sources, such as Amazon S3, Amazon Athena, or Amazon Redshift. Data Wrangler can automatically generate data insights and quality reports, which can help identify and fix missing values, outliers, and anomalies in the data. Data Wrangler also provides over 250 built-in transformations, such as resampling, interpolation, aggregation, and filtering, which can be applied to the data with a point-and-click interface. Data Wrangler can also export the prepared data to different destinations, such as Amazon S3, Amazon SageMaker Feature Store, or Amazon SageMaker Pipelines, for further modeling and analysis. Data Wrangler is integrated with Amazon SageMaker Studio, a web-based IDE for machine learning, which makes it easy to access and use the tool. Data Wrangler is a serverless and fully managed service, which means the data scientist does not need to provision, configure, or manage any infrastructure or clusters.

Option A is incorrect because Amazon EMR Serverless is a serverless option for running big data analytics applications using open-source frameworks, such as Apache Spark. However, using Amazon EMR Serverless would require the data scientist to write PySpark code to perform the data preparation tasks, such as resampling, imputation, and aggregation. This would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.

Option B is incorrect because AWS Glue DataBrew is another visual data preparation tool that can be used to clean and normalize data without writing code. However, DataBrew does not support time-series data as a data type, and does not provide built-in transformations for resampling, interpolation, or aggregation of time-series data. Therefore, using DataBrew would not meet the requirements of the use case.

Option D is incorrect because using Amazon SageMaker Studio Notebook with Pandas would also require the data scientist to write Python code to perform the data preparation tasks. Pandas is a popular Python library for data analysis and manipulation, which supports time-series data and provides various methods for resampling, interpolation, and aggregation. However, using Pandas would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.

1: Amazon SageMaker Data Wrangler documentation

2: Amazon EMR Serverless documentation

3: AWS Glue DataBrew documentation

4: Pandas documentation

To leverage the NVIDIA GPUs on Amazon EC2 P3 instances for training a custom ResNet model using Amazon SageMaker, 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. NVIDIA-Docker can automatically configure the Docker container with the necessary drivers, libraries, and environment variables to access the NVIDIA GPUs. NVIDIA-Docker can also isolate the GPU resources and ensure that each container has exclusive access to a GPU.

To build a Docker container that is NVIDIA-Docker compatible, the Machine Learning Specialist needs to follow these steps:

Install the NVIDIA Container Toolkit on the host machine that runs Docker. This toolkit includes the NVIDIA Container Runtime, which is a modified version of the Docker runtime that supports GPU hardware.

Use the base image provided by NVIDIA as the first line of the Dockerfile. The base image contains the NVIDIA drivers and CUDA toolkit that are required for GPU-accelerated applications. The base image can be specified as FROM nvcr.io/nvidia/cuda:tag, where tag is the version of CUDA and the operating system.

Install the required dependencies and frameworks for the ResNet model, such as PyTorch, torchvision, etc., in the Dockerfile.

Copy the ResNet model code and any other necessary files to the Docker container in the Dockerfile.

Build the Docker image using the docker build command.

Push the Docker image to a repository, such as Amazon Elastic Container Registry (Amazon ECR), using the docker push command.

Specify the Docker image URI and the instance type (ml.p3.xlarge) in the Amazon SageMaker CreateTrainingJob request body.

The other options are not valid or sufficient for building a Docker container that can leverage the NVIDIA GPUs on Amazon EC2 P3 instances. Bundling the NVIDIA drivers with the Docker image is not a good option, as it can cause driver conflicts and compatibility issues with the host machine and the NVIDIA GPUs. Organizing the Docker container’s file structure to execute on GPU instances is not a good option, as it does not ensure that the Docker container can access the NVIDIA GPUs and the CUDA toolkit. Setting the GPU flag in the Amazon SageMaker CreateTrainingJob request body is not a good option, as it does not apply to custom Docker containers, but only to built-in algorithms and frameworks that support GPU instances.

The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).

The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.

The XGBoost algorithm can also use different evaluation metrics to optimize the model performance. The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives. A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.

Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.

XGBoost Parameters

XGBoost for Imbalanced Classification