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GIAC GMLE Exam Syllabus Topics:

SectionObjectives
Model Evaluation and Optimization- Model tuning
  • 1. Cross-validation techniques
    • 2. Hyperparameter optimization
      - Evaluation metrics
      • 1. ROC-AUC and confusion matrix
        • 2. Accuracy, precision, recall, F1-score
          Machine Learning Engineering and Deployment- Deployment considerations
          • 1. Monitoring and maintenance
            • 2. Model serving
              - ML pipelines
              • 1. Automation of ML workflows
                • 2. Training and validation workflows
                  Machine Learning Foundations- Core ML concepts
                  • 1. Bias-variance tradeoff
                    • 2. Supervised vs unsupervised learning
                      - Mathematical and statistical fundamentals
                      • 1. Statistical inference concepts
                        • 2. Linear algebra and probability basics
                          Advanced Topics- Deep learning basics
                          • 1. Neural network fundamentals
                            • 2. Common architectures overview
                              - MLOps concepts
                              • 1. CI/CD for ML models
                                • 2. Model governance and reproducibility
                                  Machine Learning Models- Supervised learning models
                                  • 1. Regression models
                                    • 2. Classification models
                                      - Unsupervised learning
                                      • 1. Clustering algorithms
                                        • 2. Dimensionality reduction
                                          Data Preparation and Feature Engineering- Data preprocessing
                                          • 1. Data cleaning and normalization
                                            • 2. Handling missing data
                                              - Feature engineering
                                              • 1. Encoding categorical variables
                                                • 2. Feature selection techniques

                                                  GIAC Machine Learning Engineer Sample Questions:

                                                  Question 1

                                                  Which Python library is commonly used for data manipulation and analysis in machine learning?
                                                  Response:

                                                  A. Keras
                                                  B. Matplotlib
                                                  C. TensorFlow
                                                  D. Pandas


                                                  Question 2

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

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


                                                  Question 3

                                                  What is the scikit-learn library in Python best used for?
                                                  Response:

                                                  A. High-performance computing
                                                  B. Machine learning model development
                                                  C. Large-scale data processing
                                                  D. Advanced data visualization


                                                  Question 4

                                                  What is pandas.DataFrame.groupby() typically used for in data analysis with Python?
                                                  Response:

                                                  A. Sorting data in ascending or descending order
                                                  B. Splitting the data into groups based on some criteria
                                                  C. Concatenating multiple data frames
                                                  D. Performing element-wise operations on data frames


                                                  Question 5

                                                  Which of the following best describes linear regression?
                                                  Response:

                                                  A. A neural network technique
                                                  B. A technique for predicting a continuous target variable based on input features
                                                  C. A method for classifying data into categories
                                                  D. A clustering algorithm


                                                  Solutions:

                                                  Question 1
                                                  Answer: D
                                                  Question 2
                                                  Answer: A,D
                                                  Question 3
                                                  Answer: B
                                                  Question 4
                                                  Answer: B
                                                  Question 5
                                                  Answer: B

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