MLS-C01 (Page 3)

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment.The company has collected data from a large number of sources with thousands of raw attributes.Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.Which feature engineering technique should the Data Scientist use to meet the objectives?Read More →

A trucking company is collecting live image data from its fleet of trucks across the globe.The data is growing rapidly and approximately 100 GB of new data is generated every day.The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.Which storage option provides the most processing flexibility and will allow access control with IAM?Read More →

A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector.The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk.The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.Which approach will provide the MAXIMUM performance boost?Read More →

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series.Engineers want to detect critical manufacturing defects in near- real time during testing.All of the data needs to be stored for offline analysis.What approach would be the MOST effective to perform near-real time defect detection?Read More →

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud.The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.The Data Scientist has been given the following requirements to the cloud solution:✑ Combine multiple data sources.✑ Reuse existing PySpark logic.✑ Run the solution on the existing schedule.✑ Minimize the number of servers that will need to be managed.Which architecture should the Data Scientist use to build this solution?Read More →

A data scientist is building a linear regression model.The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean.Which data transformation will give the data scientist the ability to apply a linear regression model?Read More →

A data scientist stores financial datasets in Amazon S3.The data scientist uses Amazon Athena to query the datasets by using SQL.The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model.The data scientist wants to obtain inferences from the model at the SageMaker endpoint.However, when the data scientist attempts to invoke the SageMaker endpoint, the data scientist receives SQL statement failures.The data scientist’s IAM user is currently unable to invoke the SageMaker endpoint.Which combination of actions will give the data scientist’s IAM user the ability to invoke the SageMaker endpoint? (Choose three.)Read More →

A data engineer is evaluating customer data in Amazon SageMaker Data Wrangler.The data engineer will use the customer data to create a new model to predict customer behavior.The engineer needs to increase the model performance by checking for multicollinearity in the dataset.Which steps can the data engineer take to accomplish this with the LEAST operational effort? (Choose two.)Read More →

An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website.The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.The historical transactions data is in a .csv file that is stored in Amazon S3.The data contains features such as the user’s IP address, navigation time, average time on each page, and the number of clicks for each session.There is no label in the data to indicate if a transaction is anomalous.Which models should the company use in combination to detect anomalous transactions? (Choose two.)Read More →

A data engineer at a bank is evaluating a new tabular dataset that includes customer data.The data engineer will use the customer data to create a new model to predict customer behavior.After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.Which steps should the data engineer take to address this issue? (Choose two.)Read More →