Amazon MLS-C01 Practice Test - Questions Answers, Page 30

The machine learning model in this scenario shows signs of overfitting, as evidenced by better performance on the training dataset than on the testing dataset. Overfitting indicates that the model is capturing noise or details specific to the training data rather than general patterns.

One common approach to reduce overfitting is L2 regularization, which adds a penalty to the loss function for large weights and helps the model generalize better by smoothing out the weight distribution. By increasing the value of the L2 hyperparameter, the ML specialist can increase this penalty, helping to mitigate overfitting and improve performance on the testing dataset.

Options like increasing momentum or reducing dropout are less effective for addressing overfitting in this context.

Applying L1 regularization encourages sparsity by penalizing weights directly, often driving many weights to zero. In this case, the ML specialist observes that all weights become zero, which suggests that the L1 regularization parameter is set too high. This high value overly penalizes non-zero weights, effectively removing all features from the model.

To improve the model, the ML specialist should reduce the L1 regularization parameter, allowing some features to retain non-zero weights. This adjustment will make the model less prone to excessive sparsity, allowing it to better capture essential patterns in the data without dropping all features. Introducing L2 regularization is another approach but may not directly resolve this specific issue of all-zero weights as effectively as reducing L1.

When forecasting sales data, missing values can significantly impact model accuracy, especially for time series models. Approximately 10% of the days in this dataset lack sales data, which may cause gaps in patterns and disrupt seasonal trends. Linear interpolation is an effective technique for estimating and filling in missing data points based on adjacent known values, thus preserving the continuity of the time series.

By interpolating the missing values, the ML specialist can provide the model with a more complete and consistent dataset, potentially enhancing performance. This approach maintains the daily data granularity, which is important for accurately capturing trends at that frequency.

In this scenario, the company requires that access to the Amazon SageMaker notebook remain within the AWS internal network, avoiding the public internet. By creating a VPC interface endpoint for SageMaker, the company can ensure that traffic to the SageMaker notebook remains internal to the VPC and is accessible over a private connection. The VPC interface endpoint allows private network access to AWS services, and it operates over AWS's internal network, respecting the security and connectivity policies the company requires.

This solution requires minimal development effort compared to options involving bastion hosts or NAT gateways, as it directly provides private network access to the SageMaker notebook.

To prepare and transform historical data efficiently with minimal setup, Amazon SageMaker Data Wrangler is the optimal tool. Data Wrangler simplifies data preprocessing and exploratory data analysis (EDA) by providing a graphical interface for transformations and insights. By first uploading the CSV data to Amazon S3, the data becomes easily accessible to SageMaker and can be imported directly into Data Wrangler.

Once in Data Wrangler, the team can perform required data transformations and generate insights in a single workflow, avoiding the need for additional tools like Amazon QuickSight or further notebook configuration. This approach provides the simplest and most integrated solution for the data science team.

In a classification model, features with significantly larger scales can dominate the model training process, leading to poor performance. Normalization scales the values of continuous features to a uniform range, such as [0, 1], which prevents large-value features from disproportionately influencing the model. This is particularly beneficial for algorithms sensitive to the scale of input data, such as neural networks or distance-based algorithms.

Given that the problematic features are informative and representative of the target distribution, removing or bootstrapping these features is not advisable. Normalization will bring all features to a similar scale and improve the model's inference accuracy without losing important information.

To perform efficient exploratory data analysis (EDA) on a large dataset for anomaly detection, using the First K option in SageMaker Data Wrangler is an optimal choice. This option allows the data scientist to select the first K rows, limiting the data loaded into memory, which conserves compute resources.

Given that the First K option allows the data scientist to determine K based on domain knowledge, this approach provides a representative sample without requiring extensive compute resources. Other options like randomized sampling may not provide data samples that are as useful for initial analysis in a time-series or sequential dataset context.

Amazon Redshift ML enables in-database machine learning model creation and predictions, allowing data scientists to leverage Redshift for model training without needing to export data.

To create and run a model for customer churn prediction in Amazon Redshift ML:

Define the feature variables and target variable: Identify the columns to use as features (predictors) and the target variable (outcome) for the churn prediction model.

Create the model: Write a CREATE MODEL SQL statement, which trains the model using Amazon Redshift's integration with Amazon SageMaker and stores the model directly in Redshift.

Run predictions: Use the SQL PREDICT function to generate predictions on new data directly within Redshift.

Options B, D, and E are not required as Redshift ML handles model creation and prediction without manual data export to Amazon S3 or additional Spectrum integration.

Amazon Fraud Detector is a managed service designed to detect potentially fraudulent online activities, such as transactions. It uses machine learning and business rules to classify activities as fraudulent or legitimate, minimizing the need for custom model training. By using the Amazon Fraud Detector prediction API, the company can automatically approve or reject transactions flagged as fraudulent, implementing an efficient risk mitigation strategy without extensive operational effort.

This approach requires minimal setup and effectively allows the company to block fraudulent transactions with high confidence, addressing the business's need to balance risk mitigation and customer impact.