MLS-C01 · Question #252
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…
The correct answer is A. K-nearest neighbors (k-NN) with dimension reduction. 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…
Question
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?
Options
- AK-nearest neighbors (k-NN) with dimension reduction
- BLinear learner with early stopping
- CK-means
- DPrincipal component analysis (PCA) with the algorithm mode set to random
How the community answered
(22 responses)- A77% (17)
- B14% (3)
- C5% (1)
- D5% (1)
Explanation
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
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