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GIAC Machine Learning Engineer Sample Questions (Q39-Q44):
NEW QUESTION # 39
Overfitting in supervised learning models refers to:
Response:
- A. The process of training models on large datasets
- B. Models capturing noise in the training data as if it were a true signal
- C. Models performing equally on training and test data
- D. Models that are too simplistic to capture underlying patterns
Answer: B
NEW QUESTION # 40
Which techniques can be used to improve the performance of a linear regression model?
(Choose two)
Response:
- A. Including interaction terms between variables
- B. Increasing the number of decision trees
- C. Adding regularization, such as L1 (Lasso) or L2 (Ridge)
- D. Reducing the dataset size
Answer: A,C
NEW QUESTION # 41
Which loss function is typically used for regression problems?
Response:
- A. Kullback-Leibler divergence
- B. Cross-entropy loss
- C. Mean Squared Error (MSE)
- D. Hinge loss
Answer: C
NEW QUESTION # 42
You are building a multiple linear regression model to predict employee salaries based on years of experience, education level, and performance ratings. After training the model, you notice that it is overfitting the training data.
What techniques should you use to reduce overfitting and improve the model's generalization?
Response:
- A. Focus only on the most recent data and ignore the training set
- B. Remove regularization and increase the number of training samples
- C. Apply L2 regularization (Ridge), reduce the complexity of the model, and increase the size of the training dataset
- D. Increase the number of features and training epochs
Answer: C
NEW QUESTION # 43
How are genetic algorithms applied to optimize neural networks?
Response:
- A. By searching for the best combination of hyperparameters
- B. By speeding up backpropagation
- C. By adding additional layers to the network
- D. By reducing the learning rate
Answer: A
NEW QUESTION # 44
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