LATEST A00-406 TEST PREPARATION, A00-406 DUMP

Latest A00-406 Test Preparation, A00-406 Dump

Latest A00-406 Test Preparation, A00-406 Dump

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SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions (Q61-Q66):

NEW QUESTION # 61
What is the purpose of hyperparameter tuning in a machine learning pipeline?

  • A. To evaluate the model's predictions
  • B. To select the most important features
  • C. To train the model
  • D. To optimize the model's hyperparameters for better performance

Answer: D


NEW QUESTION # 62
When deploying a machine learning model, what is meant by "model latency"?

  • A. The time it takes to train a model
  • B. The time it takes to build a model
  • C. The time it takes for the model to make predictions once deployed
  • D. The time it takes to create synthetic data

Answer: C


NEW QUESTION # 63
What is the primary objective of model validation during the model assessment phase?

  • A. To assess the accuracy of the model
  • B. To ensure the model generalizes well to new, unseen data
  • C. To build a model from scratch
  • D. To create synthetic data

Answer: B


NEW QUESTION # 64
Which statement is true regarding decision trees and models based on ensembles of trees?

  • A. In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
  • B. A single decision tree will always be outperformed by a model based on an ensemble of trees.
  • C. In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
  • D. For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.

Answer: C


NEW QUESTION # 65
Which type of model is typically used for time-series forecasting?

  • A. K-Means Clustering
  • B. Decision Trees
  • C. AutoRegressive Integrated Moving Average (ARIMA)
  • D. Logistic Regression

Answer: C


NEW QUESTION # 66
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