HOTSPOTAn ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:· Feature splitting· Logarithmic transformation· One-hot encoding· Standardized distributionSelect the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all.Hot Area:
Answer(s): A
The correct feature engineering techniques for each feature are:13. City (name) - One-hot encodingThe city name is a categorical feature, so one-hot encoding is used to convert it into a binary vectorrepresentation for the model.14. Type_year (type of home and year the home was built) - Feature splitting This combined feature can be split into two separate features: "type of home" and "year the home wasbuilt," for more meaningful analysis.15. Size of the building (square feet or square meters) - Logarithmic transformation Logarithmic transformation can be applied to normalize the distribution if the size has a skewed distribution.
Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.Which AWS service or feature can aggregate the data from the various data sources?
Answer(s): D
Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.Which solution will meet these requirements?
Answer(s): C
Amazon SageMaker Data Wrangler is designed to preprocess, analyze, and visualize data efficiently. It provides built-in tools for anomaly detection, allowing the ML engineer to automatically identify anomalies in the dataset. Additionally, SageMaker Data Wrangler includes visualization capabilities to explore the data and results, meeting the requirements for anomaly detection and visualization in one integrated environment.
Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.Which action will meet this requirement with the LEAST operational overhead?
Transforming categorical data into numerical data is essential for ML models that require numerical input, as it allows the algorithm to process the categorical information effectively. Amazon SageMaker Data Wrangler provides an intuitive interface for data preparation, including built-in transformations like one-hot encoding and label encoding for categorical data. Using SageMaker Data Wrangler reduces operational overhead by offering an integrated environment to preprocess data without needing to write extensive code.
Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.Which solution will meet this requirement with the LEAST operational effort?
The Amazon SageMaker Data Wrangler balance data operation provides a built-in capability to handle class imbalance by oversampling the minority class or undersampling the majority class. This solution minimizes operational effort by offering an integrated, no-code/low-code approach to address the imbalance directly within SageMaker's data preparation workflow. It ensures that the dataset is balanced, improving the performance of the ML model.
Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.Which algorithm should the ML engineer use to meet this requirement?
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.What could be the reason for the reduced F1 score?
Concept drift occurs when the statistical properties of the data change over time, meaning the relationship between input features and the target variable in the production data differs from the data used during model training. This is a common reason for the degradation of a model's performance metrics, such as the F1 score, over time. In this case, changes in customer behavior or other external factors could cause the predictions to deviate from the actual outcomes, leading to a drop in the F1 score.
A company has a team of data scientists who use Amazon SageMaker AI notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker AI notebook instance.The company needs to centralize management of the team's permissions.Which solution will meet this requirement?
By creating a single IAM role with the required permissions and attaching it to each SageMaker notebook instance, the company can centralize permission management. This solution ensures that all notebook instances share the same permissions, eliminating the need to manage permissions individually for each instance or user. It aligns with AWS best practices for role-based access control and reduces operational overhead.
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