MLS-C01 Questions Bank

The MAPE of the predictor could be improved by making the following changes to the CreatePredictor API call:

Set PerformAutoML to true. This will allow Amazon Forecast to automatically evaluate different algorithms and choose the one that minimizes the objective function, which is the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses1. This option can help find a better algorithm than DeepAR+ for the given data.

Set PerformHPO to true. This will enable hyperparameter optimization (HPO), which is the process of finding the optimal values for the algorithm-specific parameters that affect the quality of the forecasts. HPO can improve the accuracy of the predictor by tuning the hyperparameters based on the training data2.

The other options are not likely to improve the MAPE of the predictor. Setting ForecastHorizon to 4 will reduce the number of time steps that the model predicts, which may not match the business requirement of predicting next month’s inventory. Setting ForecastFrequency to W for weekly will change the granularity of the forecasts, which may not be appropriate for the monthly data. Setting FeaturizationMethodName to filling will not have any effect, since there is no missing data in the dataset.

CreatePredictor - Amazon Forecast

HPOConfig - Amazon Forecast

 A receiver operating characteristic (ROC) curve is a model evaluation technique that can be used to understand how different classification thresholds will impact the model’s performance. A ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) for various values of the classification threshold. The TPR, also known as sensitivity or recall, is the proportion of positive instances that are correctly classified as positive. The FPR, also known as the fall-out, is the proportion of negative instances that are incorrectly classified as positive. A ROC curve can show the trade-off between the TPR and the FPR for different thresholds, and help the Machine Learning Specialist to select the optimal threshold that maximizes the TPR and minimizes the FPR. A ROC curve can also be used to compare the performance of different models by calculating the area under the curve (AUC), which is a measure of how well the model can distinguish between the positive and negative classes. A higher AUC indicates a better model

The agency wants to analyze the census data for population segmentation, which is a type of unsupervised learning problem that aims to group similar data points together based on their attributes. The agency can use a combination of algorithms that can perform dimensionality reduction and clustering on the data to achieve this goal.

Dimensionality reduction is a technique that reduces the number of features or variables in a dataset while preserving the essential information and relationships. Dimensionality reduction can help improve the efficiency and performance of clustering algorithms, as well as facilitate data visualization and interpretation. One of the most common algorithms for dimensionality reduction is principal component analysis (PCA), which transforms the original features into a new set of orthogonal features called principal components that capture the maximum variance in the data. PCA can help reduce the noise and redundancy in the data and reveal the underlying structure and patterns.

Clustering is a technique that partitions the data into groups or clusters based on their similarity or distance. Clustering can help discover the natural segments or categories in the data and understand their characteristics and differences. One of the most popular algorithms for clustering is k-means, which assigns each data point to one of k clusters based on the nearest mean or centroid. K-means can handle large and high-dimensional datasets and produce compact and spherical clusters.

Therefore, the combination of algorithms that would provide the appropriate insights for population segmentation are PCA and k-means. The agency can use PCA to reduce the dimensionality of the census data from 500 features to a smaller number of principal components that capture most of the variation in the data. Then, the agency can use k-means to cluster the data based on the principal components and identify the segments of the population that share similar characteristics.

Amazon SageMaker Principal Component Analysis (PCA)

Amazon SageMaker K-Means Algorithm

The best solution for this scenario is to use a multi-model endpoint in Amazon SageMaker, which allows hosting multiple models on the same endpoint and invoking them dynamically at runtime. This way, the company can reduce the operational overhead of managing multiple EC2 instances and model servers, and leverage the scalability, security, and performance of SageMaker hosting services. By using a multi-model endpoint, the company can also save on hosting costs by improving endpoint utilization and paying only for the models that are loaded in memory and the API calls that are made. To use a multi-model endpoint, the company needs to prepare a Docker container based on the open-source multi-model server, which is a framework-agnostic library that supports loading and serving multiple models from Amazon S3. The company can then create a multi-model endpoint in SageMaker, pointing to the S3 bucket containing all the models, and invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request. This solution also enables the company to add or remove models from the S3 bucket without redeploying the endpoint, and to use different versions of the same model for different cities if needed. References:

Use Docker containers to build models

Host multiple models in one container behind one endpoint

Multi-model endpoints using Scikit Learn

Multi-model endpoints using XGBoost