An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark.A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark.The data scientist wants AWS to be responsible for the operational maintenance of the container.Which solution will meet these requirements?

Package the code by extending an Amazon SageMaker scikit-learn container.

Package the code as the training script for an Amazon SageMaker scikit-learn container.

Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).

Package the code into a custom-built container. Push the container to AWS Fargate.

Package the code by extending an Amazon SageMaker scikit-learn container.

A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features.How should the ML engineer predict the contribution of each feature?

Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.

Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.

Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.

Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.

Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline. Manually add the feature scores.

A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use this dataset to train a machine learning (ML) model.The data scientist first needs to identify any potential data quality issues in the dataset. The data scientist must identify values that are missing or values that are not valid. The data scientist must also identify the number of outliers in the dataset.Which solution will meet these requirements with the LEAST operational effort?)

Leave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

Leave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

Leave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

Amazon MLS-C01 Practice Test 9 - Free Online Exam Questions 321-325

A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameter that will generate the most accurate model.

Which approach will meet these requirements with the LEAST operational overhead?

Become a Premium Member for full access

Unlock Premium Member

Amazon MLS-C01 Practice Test 9

Question

Amazon MLS-C01 Practice Tests

Practice Tests