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

 Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 . References:

How Amazon Forecast Works - Amazon Forecast

Choosing Datasets - Amazon Forecast

Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data. Some common forms of regularization for image classification are:

Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.

Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.

Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.

The other options are not likely to fix the issue of overfitting, and may even worsen it:

A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.

Making the network larger: This can increase the model capacity and complexity, which can also lead to more overfitting, as the model will have more parameters to learn and adjust to the training data.

Using a different optimizer: This can affect the speed and stability of the training process, but not necessarily the generalization ability of the model. The choice of optimizer depends on the characteristics of the data and the model, and there is no guarantee that a different optimizer will prevent overfitting.

References:

Regularization (machine learning)

Image Classification: Regularization

How to Reduce Overfitting With Dropout Regularization in Keras

Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:

Principal Component Analysis (PCA) Algorithm - Amazon SageMaker

Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog

Machine Learning- Prinicipal Component Analysis | i2tutorials

 Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.

Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.

References:

Deploying models to Amazon SageMaker hosting services - Amazon SageMaker

Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker

UpdateEndpointWeightsAndCapacities - Amazon SageMaker