Amazon MLS-C01 Practice Test - Questions Answers, Page 20

Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 .References:

How Amazon Forecast Works - Amazon Forecast

Choosing Datasets - Amazon Forecast

The best way to ensure that provisioned resources are being utilized effectively is to redeploy the model on an M5 instance and attach Amazon Elastic Inference to the instance. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. By using Amazon Elastic Inference, you can choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need with no code changes. This way, you can avoid wasting GPU resources and pay only for what you use.

Option A is incorrect because a batch transform job is not suitable for real-time predictions. Batch transform is a high-performance and cost-effective feature for generating inferences using your trained models. Batch transform manages all of the compute resources required to get inferences. Batch transform is ideal for scenarios where you're working with large batches of data, don't need sub-second latency, or need to process data that is stored in Amazon S3.

Option C is incorrect because redeploying the model on a P3dn instance would not improve the resource utilization. P3dn instances are designed for distributed machine learning and high performance computing applications that need high network throughput and packet rate performance. They are not optimized for inference workloads.

Option D is incorrect because deploying the model onto an Amazon ECS cluster using a P3 instance would not ensure that provisioned resources are being utilized effectively. Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. However, using Amazon ECS would not address the issue of underutilized GPU resources. In fact, it might introduce additional overhead and complexity in managing the cluster.

References:

Amazon Elastic Inference - Amazon SageMaker

Batch Transform - Amazon SageMaker

Amazon EC2 P3 Instances

Amazon EC2 P3dn Instances

Amazon Elastic Container Service

The best way to ensure that required packages are automatically available on the notebook instance for the data scientist to use is to create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance. A lifecycle configuration is a shell script that runs when you create or start a notebook instance. You can use a lifecycle configuration to customize the notebook instance by installing libraries, changing environment variables, or downloading datasets. You can also use a lifecycle configuration to automate the installation of custom Python packages that are not natively available on Amazon SageMaker.

Option A is incorrect because installing AWS Systems Manager Agent on the underlying Amazon EC2 instance and using Systems Manager Automation to execute the package installation commands is not a recommended way to customize the notebook instance. Systems Manager Automation is a feature that lets you safely automate common and repetitive IT operations and tasks across AWS resources. However, using Systems Manager Automation would require additional permissions and configurations, and it would not guarantee that the packages are installed before the notebook instance is ready to use.

Option B is incorrect because creating a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and placing the file under the /etc/init directory of each Amazon SageMaker notebook instance is not a valid way to customize the notebook instance. The /etc/init directory is used to store scripts that are executed during the boot process of the operating system, not the Jupyter notebook application. Moreover, a Jupyter notebook file is not a shell script that can be executed by the operating system.

Option C is incorrect because using the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook is not an automatic way to customize the notebook instance. This option would require the data scientist to manually run the conda commands every time they create or start a new notebook instance. This would not be efficient or convenient for the data scientist.

References:

Customize a notebook instance using a lifecycle configuration script - Amazon SageMaker

AWS Systems Manager Automation - AWS Systems Manager

Conda environments - Amazon SageMaker

The best strategy to identify fraudulent accounts is to create a FindMatches machine learning transform in AWS Glue. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This can help you improve fraud detection by finding accounts that are associated with a previously known fraudulent user. You can teach the FindMatches transform your definition of a ''duplicate'' or a ''match'' through examples, and it will use machine learning to identify other potential duplicates or matches in your dataset. You can then use the FindMatches transform in your AWS Glue ETL jobs to cleanse your data.

Option A is incorrect because there is no built-in FindDuplicates Amazon Athena query. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. However, Amazon Athena does not provide a predefined query to find duplicate records in a dataset. You would have to write your own SQL query to perform this task, which might not be as effective or accurate as using the FindMatches transform.

Option C is incorrect because creating an AWS Glue crawler to infer duplicate accounts in the source data is not a valid strategy. An AWS Glue crawler is a program that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the AWS Glue Data Catalog. A crawler does not perform any data cleansing or record matching tasks.

Option D is incorrect because searching for duplicate accounts in the AWS Glue Data Catalog is not a feasible strategy. The AWS Glue Data Catalog is a central repository to store structural and operational metadata for your data assets. The Data Catalog does not store the actual data, but rather the metadata that describes where the data is located, how it is formatted, and what it contains. Therefore, you cannot search for duplicate records in the Data Catalog.

References:

Record matching with AWS Lake Formation FindMatches - AWS Glue

Amazon Athena -- Interactive SQL Queries for Data in Amazon S3

AWS Glue Crawlers - AWS Glue

AWS Glue Data Catalog - AWS Glue

The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.

The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non-fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.

The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.

References:

XGBoost Parameters - Amazon Machine Learning

Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

The data scientist should adjust hyperparameters related to the attention mechanism to resolve the problem. The attention mechanism is a technique that allows the decoder to focus on different parts of the input sequence when generating the output sequence. It helps the model cope with long input sequences and improve the translation quality. The Amazon SageMaker seq2seq algorithm supports different types of attention mechanisms, such as dot, general, concat, and mlp. The data scientist can use the hyperparameter attention_type to choose the type of attention mechanism. The data scientist can also use the hyperparameter attention_coverage_type to enable coverage, which is a mechanism that penalizes the model for attending to the same input positions repeatedly. By adjusting these hyperparameters, the data scientist can fine-tune the attention mechanism and reduce the number of false negative predictions by the model.

References:

Sequence-to-Sequence Algorithm - Amazon SageMaker

Attention Mechanism - Sockeye Documentation

The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).

The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.

The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company's goal.

References:

Metrics for Imbalanced Classification in Python - Machine Learning Mastery

Precision-Recall - scikit-learn

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

The engineer should create a private workforce and manifest file, and then create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. This will allow the engineer to build the labeling pipeline with the least effort.

A private workforce is a group of workers that you manage and who have access to your labeling tasks. You can use a private workforce to label sensitive data that requires confidentiality, such as medical images. You can create a private workforce by using Amazon Cognito and inviting workers by email. You can also use AWS Single Sign-On or your own authentication system to manage your private workforce.

A manifest file is a JSON file that lists the Amazon S3 locations of your input data. You can use a manifest file to specify the data objects that you want to label in your labeling job. You can create a manifest file by using the AWS CLI, the AWS SDK, or the Amazon SageMaker console.

A labeling job is a process that sends your input data to workers for labeling. You can use the Amazon SageMaker console to create a labeling job and choose from several built-in task types, such as image classification, text classification, semantic segmentation, and bounding box. A bounding box task type allows workers to draw boxes around objects in an image and assign labels to them. This is suitable for object detection tasks, such as identifying areas of concern on CT scans.

References:

Create and Manage Workforces - Amazon SageMaker

Use Input and Output Data - Amazon SageMaker

Create a Labeling Job - Amazon SageMaker

Bounding Box Task Type - Amazon SageMaker