Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters. References:

Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.

K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

Precision measures the proportion of true positive predictions out of all positive predictions made by the model. In this scenario, since the cost of a false positive is high, it is critical to minimize false positives, which means prioritizing high precision.

From AWS documentation:

“Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. High precision relates to a low false positive rate.”

— AWS ML Exam Guide – Evaluation Metrics

Recall (C) would focus on minimizing false negatives, which is not important here as missing positives has no cost. F1 (D) balances precision and recall, but the question emphasizes prioritizing precision only.

The best Amazon SageMaker algorithm for this task is Image Classification - TensorFlow. This algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub. Transfer learning allows the company to fine-tune one of the available pretrained models on their own dataset, even if a large amount of image data is not available. The image classification algorithm takes an image as input and outputs a probability for each provided class label. The company can choose from a variety of models, such as MobileNet, ResNet, or Inception, depending on their accuracy and speed requirements. The algorithm also supports distributed training, data augmentation, and hyperparameter tuning.

Image Classification - TensorFlow - Amazon SageMaker

Amazon SageMaker Provides New Built-in TensorFlow Image Classification Algorithm

Image Classification with ResNet :: Amazon SageMaker Workshop

Image classification on Amazon SageMaker | by Julien Simon - Medium