MLS-C01 Reviews Questions

The Data Science team is facing a problem of imbalanced data, where the positive class (paid users) is much less frequent than the negative class (non-paid users). This can cause the random forest model to be biased towards the majority class and have poor performance on the minority class. To mitigate this issue, the Data Science team can try the following approaches:

• C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data. This is a technique called data augmentation, which can help increase the size and diversity of the training data for the minority class. This can help the random forest model learn more features and patterns from the positive class and reduce the imbalance ratio.
• D. Change the cost function so that false negatives have a higher impact on the cost value than false positives. This is a technique called cost-sensitive learning, which can assign different weights or costs to different classes or errors. By assigning a higher cost to false negatives (predicting non-paid when the user is actually paid), the random forest model can be more sensitive to the minority class and try to minimize the misclassification of the positive class.

References:

• Bagging and Random Forest for Imbalanced Classification
• Surviving in a Random Forest with Imbalanced Datasets
• machine learning - random forest for imbalanced data? - Cross Validated
• Biased Random Forest For Dealing With the Class Imbalance Problem

• To read data from an Amazon S3 bucket that is protected with server-side encryption using AWS KMS, the Amazon SageMaker notebook instance needs to have an IAM role that has permission to access the S3 bucket and the KMS key. The IAM role is an identity that defines the permissions for the notebook instance to interact with other AWS services. The IAM role can be assigned to the notebook instance when it is created or updated later.
• The KMS key policy is a document that specifies who can use and manage the KMS key. The KMS key policy can grant permission to the IAM role of the notebook instance to decrypt the data in the S3 bucket. The KMS key policy can also grant permission to other principals, such as AWS accounts, IAM users, or IAM roles, to use the KMS key for encryption and decryption operations.
• Therefore, the Machine Learning Specialist should assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role. This way, the notebook instance can use the IAM role credentials to access the S3 bucket and the KMS key, and read the encrypted data from the S3 bucket.

References:

• Create an IAM Role to Grant Permissions to Your Notebook Instance
• Using Key Policies in AWS KMS

• The Data Scientist needs to migrate an existing on-premises ETL process to the cloud, using a solution that can combine multiple data sources, reuse existing PySpark logic, run on the existing schedule, and minimize the number of servers that need to be managed. The best architecture for this scenario is to use AWS Glue, which is a serverless data integration service that can create and run ETL jobs on AWS.
• AWS Glue can perform the following tasks to meet the requirements:
• Therefore, the Data Scientist should use the following architecture to build the cloud solution:

References:

• What Is AWS Glue?
• AWS Glue Components
• AWS Glue Studio
• AWS Glue Triggers

The prior probability distribution for the discrete random variable that represents the number of minutes New Yorkers wait for a bus is a Poisson distribution. A Poisson distribution is suitable for modeling the number of events that occur in a fixed interval of time or space, given a known average rate of occurrence. In this case, the event is waiting for a bus, the interval is 10 minutes, and the average rate is 3 minutes. The Poisson distribution can capture the variability of the waiting time, which can range from 0 to 10 minutes, with different probabilities.

References:

• 1: Poisson Distribution - Amazon SageMaker
• 2: Poisson Distribution - Wikipedia