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NEW QUESTION 45
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company’s data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model’s testing accuracy.
Which process will improve the testing accuracy the MOST?
- A. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
- B. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.
- C. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
- D. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.
NEW QUESTION 46
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?
- A. An Amazon RDS database with hourly table partitions
- B. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
- C. An Amazon EBS-backed Amazon EC2 instance with hourly directories
- D. An Amazon S3 data lake with hourly object prefixes
NEW QUESTION 47
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?
- A. Drop the test samples with missing full review text fields, and then run through the test set.
- B. Use an algorithm that handles missing data better than decision trees.
- C. Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
- D. Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field.
NEW QUESTION 48
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