Curious about Actual Amazon Specialty (MLS-C01) Exam Questions?

Correct : A

Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability. Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.

The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.

References:

* Stratified Sampling for Imbalanced Datasets

* Imbalanced Data

* Tour of Data Sampling Methods for Imbalanced Classification

Correct : A, C

The Hyperband tuning strategy is a multi-fidelity based tuning strategy that dynamically reallocates resources to the most promising hyperparameter configurations. Hyperband uses both intermediate and final results of training jobs to stop under-performing jobs and reallocate epochs to well-utilized hyperparameter configurations. Hyperband can provide up to three times faster hyperparameter tuning compared to other strategies1. Setting a lower value for the MaxNumberOfTrainingJobs parameter can also reduce the computation time for the hyperparameter tuning job by limiting the number of training jobs that the tuning job can launch. This can help avoid unnecessary or redundant training jobs that do not improve the objective metric.

The other options are not effective ways to reduce the computation time for the hyperparameter tuning job. Increasing the number of hyperparameters will increase the complexity and dimensionality of the search space, which can result in longer computation time and lower performance. Using the grid search tuning strategy will also increase the computation time, as grid search methodically searches through every combination of hyperparameter values, which can be very expensive and inefficient for large search spaces. Setting a lower value for the MaxParallelTrainingJobs parameter will reduce the number of training jobs that can run in parallel, which can slow down the tuning process and increase the waiting time.

References:

* How Hyperparameter Tuning Works

* Best Practices for Hyperparameter Tuning

* HyperparameterTuner

* Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband

Correct : A

The SageMaker CreatePresignedDomainUrl API is the best option to meet the requirements of the company. This API creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This API can only be called when the authentication mode equals IAM, which means the company can use its existing IdP to authenticate users. The company can use the DynamoDB table to store the domain IDs and user profile names for each department, and use the proxy application to query the table and generate the presigned URL for the appropriate domain according to the user's credentials. The presigned URL is valid only for a specified duration, which can be set by the SessionExpirationDurationInSeconds parameter. This can help enhance the security and prevent unauthorized access to the domains.

The other options are not suitable for the company's requirements. The SageMaker CreateHumanTaskUi API is used to define the settings for the human review workflow user interface, which is not related to accessing the SageMaker Studio domains. The SageMaker ListHumanTaskUis API is used to return information about the human task user interfaces in the account, which is also not relevant to the company's use case. The SageMaker CreatePresignedNotebookInstanceUrl API is used to create a URL to connect to the Jupyter server from a notebook instance, which is different from accessing the SageMaker Studio domain.

References:

* CreatePresignedDomainUrl

* CreatePresignedNotebookInstanceUrl

* CreateHumanTaskUi

* ListHumanTaskUis

Correct : B

The best solution to meet the requirements is to apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training. SMOTE is a technique that generates synthetic samples for the minority class by interpolating between existing samples. This can help balance the class distribution and provide more information to the model. SMOTE can improve the performance of the model on the minority class, which is the class of interest in churn prediction. SMOTE can be applied using the SageMaker Data Wrangler, which provides a built-in analysis for oversampling the minority class1.

The other options are not effective solutions for the problem. Applying anomaly detection to remove outliers from the training dataset before training may not improve the model's accuracy, as outliers may not be the main cause of the false results. Moreover, removing outliers may reduce the diversity of the data and make the model less robust. Applying normalization to the features of the training dataset before training may improve the model's convergence and stability, but it does not address the class imbalance issue. Normalization can also be applied using the SageMaker Data Wrangler, which provides a built-in transformation for scaling the features2. Applying undersampling to the training dataset before training may reduce the class imbalance, but it also discards potentially useful information from the majority class. Undersampling can also result in underfitting and high bias for the model.

References:

* Analyze and Visualize

* Transform and Export

* SMOTE for Imbalanced Classification with Python

* Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

Correct : A

The best solution to verify the suitability of an ARIMA approach is to use Amazon SageMaker Data Wrangler. Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Seasonal-Trend decomposition, which can be used to decompose a time series into its trend, seasonal, and residual components. This analysis can help the developer understand the patterns and characteristics of the time series, such as stationarity, seasonality, and autocorrelation, which are important for choosing an appropriate ARIMA model. Data Wrangler also provides built-in transformations that can help the developer handle missing data, such as imputing with mean, median, mode, or constant values, or dropping rows with missing values. Imputing missing data can help avoid gaps and irregularities in the time series, which can affect the ARIMA model performance. Data Wrangler also allows the developer to export the prepared data and the analysis code to various destinations, such as SageMaker Processing, SageMaker Pipelines, or SageMaker Feature Store, for further processing and modeling.

The other options are not suitable for verifying the suitability of an ARIMA approach. Amazon SageMaker Autopilot is a feature-set that automates key tasks of an automatic machine learning (AutoML) process. It explores the data, selects the algorithms relevant to the problem type, and prepares the data to facilitate model training and tuning. However, Autopilot does not support ARIMA as a machine learning problem type, and it does not provide any visualization or analysis of the time series data. Resampling data by using the aggregate daily total can reduce the granularity and resolution of the time series, which can affect the ARIMA model accuracy and applicability.

References:

* Analyze and Visualize

* Transform and Export

* Amazon SageMaker Autopilot

* ARIMA Model -- Complete Guide to Time Series Forecasting in Python