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GIAC Machine Learning Engineer Sample Questions:

1. What is Principal Component Analysis (PCA) a technique used for in unsupervised learning?
Response:

A) Improving the accuracy of classification models
B) Clustering similar data points
C) Reducing the dimensionality of data
D) Predicting future trends in data

2. How does 'L1 regularization' differ from 'L2 regularization' in machine learning?
Response:

A) L1 is used for regression; L2 is used for classification
B) L1 adds the absolute value of coefficients; L2 adds the square of coefficients
C) L1 optimizes the learning rate; L2 optimizes the batch size
D) L1 is for neural networks; L2 is for decision trees

3. Which of the following are key characteristics of genetic algorithms used in neural network optimization?
(Choose two)
Response:

A) Evolution through mutation and crossover
B) Fitness evaluation based on a predefined objective
C) Random initialization of weights
D) Manual selection of hyperparameters

4. Which of the following are common supervised learning algorithms?
(Choose two)
Response:

A) Linear Regression
B) Random Forest
C) k-means
D) DBSCAN

5. You are working on a classification problem where you are using Bayes' Theorem to predict whether a new email is spam based on specific keywords. You have prior probabilities of an email being spam or not, and likelihoods for certain keywords appearing in spam emails.
How should you use Bayes' Theorem to calculate the posterior probability that the new email is spam?
Response:

A) Use the prior probabilities and likelihoods of the keywords appearing in both spam and non-spam emails to calculate the posterior probability that the email is spam
B) Ignore the likelihoods and use only the prior probability
C) Manually review the email to classify it as spam or not
D) Use the frequency of keywords only in spam emails to calculate the probability

Solutions: