Amazon Web Services Updated MLS-C01 Exam Questions and Answers by denny

In Amazon SageMaker, hyperparameter tuning jobs optimize model performance by adjusting hyperparameters. Amazon SageMaker’s hyperparameter tuning supports completion criteria settings that enable efficient management of tuning resources. In this scenario, the ML specialist aims to set a completion criterion that will terminate the tuning job as soon as SageMaker detects that further improvements in the objective metric are unlikely to exceed 1%.

The CompleteOnConvergence setting is designed for such requirements. This criterion enables the tuning job to automatically stop when SageMaker determines that additional hyperparameter evaluations are unlikely to improve the objective metric beyond a certain threshold, allowing for efficient tuning completion. The convergence process relies on an internal optimization algorithm that continuously evaluates the objective metric during tuning and stops when performance stabilizes without further improvement.

This is supported by AWS documentation, which explains that CompleteOnConvergence is an efficient way to manage tuning by stopping unnecessary evaluations once the model performance stabilizes within the specified threshold.

The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances can speed up training and lower costs by taking advantage of the spare EC2 capacity. The company does not need to make any code changes to use Spot Instances, as it can simply enable the managed spot training option in the SageMaker training job configuration. The company also does not need to implement model checkpoints, as it is using only one epoch for training, which means the model will not resume from a previous state1.

The other options are not suitable because:

Option A: Configuring the SageMaker training job to use Pipe mode instead of File mode will not speed up training or lower costs significantly. Pipe mode is a data ingestion mode that streams data directly from S3 to the training algorithm, without copying the data to the local storage of the training instance. Pipe mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, Pipe mode may require some code changes to handle the streaming data, depending on the training algorithm2.

Option B: Configuring the SageMaker training job to use FastFile mode instead of File mode will not speed up training or lower costs significantly. FastFile mode is a data ingestion mode that copies data from S3 to the local storage of the training instance in parallel with the training process. FastFile mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, FastFile mode is only available for distributed training jobs that use multiple instances, which is not the case for the company3.

Option D: Configuring the SageMaker training job to use Spot Instances and implementing model checkpoints will not meet the requirements without the need to make any code changes. Model checkpoints are a feature that allows the training job to save the model state periodically to S3, and resume from the latest checkpoint if the training job is interrupted. Model checkpoints can help to avoid losing the training progress and ensure the model convergence, but they require some code changes to implement the checkpointing logic and the resuming logic4.

1: Managed Spot Training - Amazon SageMaker

2: Pipe Mode - Amazon SageMaker

3: FastFile Mode - Amazon SageMaker

4: Checkpoints - Amazon SageMaker

The best approach to identify and visualize the natural groupings for the numerical columns across all customers is to embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot. t-SNE is a dimensionality reduction technique that can project high-dimensional data into a lower-dimensional space, while preserving the local structure and distances of the data points. A scatter plot can then show the clusters of data points in the reduced space, where each point represents a customer and the color indicates the cluster membership. This approach can help the Specialist quickly explore the patterns and similarities among the customers based on their numerical features.

The other options are not as effective or efficient as the t-SNE approach. Running k-means for different values of k and creating an elbow plot can help determine the optimal number of clusters, but it does not provide a visual representation of the clusters or the customers. Embedding the numerical features using t-SNE and creating a line graph does not make sense, as a line graph is used to show the change of a variable over time, not the distribution of data points in a space. Running k-means for different values of k and creating box plots for each numerical column within each cluster can provide some insights into the statistics of each cluster, but it is very time-consuming and cumbersome to create and compare thousands of box plots. References:

Dimensionality Reduction - Amazon SageMaker

Visualize high dimensional data using t-SNE - Amazon SageMaker

The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.

One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series. DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.

DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.

Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.

DeepAR Forecasting Algorithm - Amazon SageMaker

Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting