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Pass Guaranteed A00-406 - SAS® Viya® Supervised Machine Learning Pipelines Latest Exam Overview
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SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions (Q85-Q90):
NEW QUESTION # 85
What is feature engineering in the context of machine learning pipelines?
- A. Testing the model's performance
- B. Creating new features from existing data
- C. Applying the model to new data
- D. Building a machine learning model from scratch
Answer: B
NEW QUESTION # 86
What is the primary goal of building models in data science and machine learning?
- A. Data visualization
- B. Feature engineering
- C. Data cleaning
- D. Making predictions or inferences from data
Answer: D
NEW QUESTION # 87
Which of the following best describes unstructured data?
- A. Data that is organized in rows and columns
- B. Data that is difficult to process and lacks a predefined structure
- C. Data stored in a relational database
- D. Data with a clear schema
Answer: B
NEW QUESTION # 88
What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?
- A. To prevent overfitting and reduce model complexity
- B. To speed up model training
- C. To increase model complexity
- D. To add more features to the model
Answer: A
NEW QUESTION # 89
Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?
- A. Regularization
- B. One-Hot Encoding
- C. Principal Component Analysis (PCA)
- D. Naive Bayes
Answer: A
NEW QUESTION # 90
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