Topic 1 Question 224
A data scientist at a food production company wants to use an Amazon SageMaker built-in model to classify different vegetables. The current dataset has many features. The company wants to save on memory costs when the data scientist trains and deploys the model. The company also wants to be able to find similar data points for each test data point.
Which algorithm will meet these requirements?
K-nearest neighbors (k-NN) with dimension reduction
Linear learner with early stopping
K-means
Principal component analysis (PCA) with the algorithm mode set to random
ユーザの投票
コメント(9)
- 正解だと思う選択肢: C
should be C
👍 3Aninina2023/02/07 - 正解だと思う選択肢: A
"Training with the k-NN algorithm has three steps: sampling, dimension reduction, and index building. Sampling reduces the size of the initial dataset so that it fits into memory. For dimension reduction, the algorithm decreases the feature dimension of the data to reduce the footprint of the k-NN model in memory and inference latency." " The main objective of k-NN's training is to construct the index. The index enables efficient lookups of distances between points whose values or class labels have not yet been determined and the k nearest points to use for inference."
https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html
👍 3Jerry842023/02/14 - 正解だと思う選択肢: A
option A suggests using the k-nearest neighbors (k-NN) algorithm with dimension reduction. The k-NN algorithm can be used for classification tasks and dimension reduction can help reduce memory costs. Additionally, k-NN can be used for finding similar data points.
K-NN is a simple algorithm that works well with high-dimensional data and can find similar data points.
👍 2AjoseO2023/02/20
シャッフルモード