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GIAC GMLE Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Advanced Topics | - MLOps concepts
|
| Topic 2: Machine Learning Engineering and Deployment | - Deployment considerations
|
| Topic 3: Machine Learning Foundations | - Core ML concepts
|
| Topic 4: Model Evaluation and Optimization | - Model tuning
|
| Topic 5: Data Preparation and Feature Engineering | - Feature engineering
|
| Topic 6: Machine Learning Models | - Unsupervised learning
|
GIAC Machine Learning Engineer Sample Questions:
Which Python library is most commonly used for data manipulation in machine learning?
Response:
- A. Pandas
- B. TensorFlow
- C. Scikit-learn
- D. Matplotlib
Correct Answer: A 🗳️
You are using a CNN to classify images in a dataset. After several epochs of training, you notice that the model performs well on the training set but poorly on the validation set. This suggests overfitting.
What steps should you take to improve the generalization of the model?
Response:
- A. Implement dropout in the fully connected layers, apply data augmentation to increase the variability of the training data, and consider early stopping to prevent overfitting
- B. Reduce the size of the dataset and increase the number of convolutional layers
- C. Remove padding to simplify the architecture
- D. Train the model for more epochs and reduce the use of regularization
Correct Answer: A 🗳️
What does 'grid search' refer to in hyperparameter tuning?
Response:
- A. A technique to search exhaustively through a specified subset of hyperparameters
- B. A strategy for reducing the number of hyperparameters
- C. The process of randomly selecting hyperparameters for testing
- D. A method to visualize the performance of different models
Correct Answer: A 🗳️
Which of the following is a key advantage of Convolutional Neural Networks (CNNs) in image classification?
Response:
- A. They reduce the need for feature engineering by learning features automatically from images
- B. They work best with time-series data
- C. They are easy to interpret
- D. They perform well with structured data
Correct Answer: A 🗳️
What is the main advantage of using a 'bagging' approach in ensemble learning?
Response:
- A. To enhance the speed of training models
- B. To reduce the variance of the model
- C. To increase the bias in the model
- D. To optimize individual model parameters
Correct Answer: B 🗳️
