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GIAC GMLE Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Fundamentals | - Supervised and unsupervised learning
|
| Anomaly Detection and Optimization | - Security-focused ML applications
|
| Data Acquisition and Exploration | - Data acquisition
|
| Statistics and Probability | - Inferential statistics
|
| Neural Networks and Deep Learning | - Deep learning concepts
|
| Python for Machine Learning | - Python scripting
|
GIAC Machine Learning Engineer Sample Questions:
What is the purpose of a p-value in hypothesis testing?
Response:
- A. To measure the correlation between variables
- B. To test for multicollinearity
- C. To calculate the mean of a dataset
- D. To determine the probability of observing the data given that the null hypothesis is true
Correct Answer: D 🗳️
Which is not a typical use case for CNNs?
Response:
- A. Natural language processing
- B. Object detection
- C. Facial recognition
- D. Image classification
Correct Answer: A 🗳️
Which of the following is a supervised learning algorithm commonly used for classification tasks?
Response:
- A. Principal Component Analysis (PCA)
- B. Agglomerative clustering
- C. Support Vector Machines (SVM )
- D. k-means
Correct Answer: C 🗳️
You are tasked with building a classification model to predict whether customers will purchase a product based on their browsing behavior. You decide to use a Random Forest classifier. After training the model, you notice that the training accuracy is very high, but the validation accuracy is much lower.
What might be causing this, and how can you improve the model's generalization?
Response:
- A. The features are not useful; removing all but one feature will reduce overfitting
- B. The training data is too large; reducing the training set size will improve the model's performance
- C. The model is underfitting the data; increasing the number of features and trees will improve performance
- D. The model is likely overfitting to the training data; applying cross-validation and reducing the number of trees in the forest or using regularization techniques can help improve the generalization to unseen data
Correct Answer: D 🗳️
What is the purpose of a validation set in machine learning?
Response:
- A. To fine-tune the model's hyperparameters
- B. To prevent overfitting
- C. To train the model
- D. To test the model on unseen data
Correct Answer: A 🗳️
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