GMLE問題集で2025年最新のGIAC GMLE試験問題
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質問 # 54
What does 'root mean squared error' (RMSE) measure in regression models?
Response:
- A. The variance explained by the model
- B. The standard deviation of the residuals
- C. The average of the absolute errors
- D. The square root of the average of squared differences between prediction and actual observa
正解：D
質問 # 55
Which of the following is a commonly used algorithm for clustering?
Response:
- A. k-means
- B. Support Vector Machines
- C. Decision Trees
- D. Logistic Regression
正解：A
質問 # 56
You have a large dataset stored as a CSV file, and you want to load, clean, and prepare it for a machine learning model using Python. What steps should you take using Pandas to clean the data and remove any rows with missing values?
Response:
- A. Use np.load() to load the data and ignore any missing values
- B. Load the data with pd.read_csv() and manually remove missing values from the file
- C. Visualize the missing values with Matplotlib before cleaning the data
- D. Use the pd.read_csv() function to load the data, followed by the dropna() function to remove rows with missing values, and the describe() function to get summary statistics for further analysis
正解：D
質問 # 57
Which of the following metrics is commonly used to evaluate the performance of a regression model?
Response:
- A. Precision
- B. F1 score
- C. Mean Squared Error (MSE)
- D. Confusion matrix
正解：C
質問 # 58
Which of the following are common activation functions used in neural networks?
(Choose two)
Response:
- A. Weight initialization
- B. Sigmoid function
- C. Batch normalization
- D. ReLU (Rectified Linear Unit)
正解：B、D
質問 # 59
What does 'gradient boosting' refer to in ensemble learning?
Response:
- A. A technique to decrease the training time of models
- B. A method of combining weak learners to create a strong learner
- C. An approach to reduce the variance of the model
- D. A strategy to reduce the complexity of models
正解：B
質問 # 60
Which techniques are commonly used to evaluate the quality of clustering results?
(Choose two)
Response:
- A. ROC curve
- B. Elbow method
- C. Silhouette score
- D. Confusion matrix
正解：B、C
質問 # 61
Which of the following are common applications of Bayes' Theorem in machine learning?
(Choose two)
Response:
- A. Calculating the posterior probability in classification tasks
- B. Building decision trees
- C. Developing recommendation systems
- D. Creating Naive Bayes classifiers
正解：A、D
質問 # 62
The 'Pandas' library in Python is primarily used for:
Response:
- A. Real-time data streaming
- B. Implementing reinforcement learning algorithms
- C. Data manipulation and analysis
- D. Building neural network models
正解：C
質問 # 63
What is the purpose of 'regularization' in machine learning models?
Response:
- A. To increase the number of features in the model
- B. To enhance the visualization of the model's performance
- C. To speed up the training process
- D. To add constraints to the model to reduce overfitting
正解：D
質問 # 64
In a fully connected neural network, what is the primary role of the activation function?
Response:
- A. To optimize the learning rate
- B. To compute the error
- C. To introduce non-linearity into the model
- D. To reduce overfitting
正解：C
質問 # 65
Which of the following is a data manipulation technique commonly applied to prepare data for machine learning models?
Response:
- A. Normalization
- B. Hyperparameter tuning
- C. Overfitting
- D. Data augmentation
正解：A
質問 # 66
Which of the following is a key advantage of Convolutional Neural Networks (CNNs) in image classification?
Response:
- A. They are easy to interpret
- B. They perform well with structured data
- C. They reduce the need for feature engineering by learning features automatically from images
- D. They work best with time-series data
正解：C
質問 # 67
How does 'L1 regularization' differ from 'L2 regularization' in machine learning?
Response:
- A. L1 adds the absolute value of coefficients; L2 adds the square of coefficients
- B. L1 is used for regression; L2 is used for classification
- C. L1 is for neural networks; L2 is for decision trees
- D. L1 optimizes the learning rate; L2 optimizes the batch size
正解：A
質問 # 68
What is the role of padding in a convolutional layer?
Response:
- A. To preserve the spatial dimensions of the input data
- B. To normalize the inputs
- C. To increase the number of parameters in the model
- D. To prevent overfitting
正解：A
質問 # 69
You are analyzing the performance of machine learning models by comparing their error rates. After calculating the mean and standard deviation of the errors for each model, you notice that one model has a high standard deviation compared to the others.
What does this suggest about the model's performance, and what steps can you take to improve it?
Response:
- A. The model's performance is optimal, and no changes are needed
- B. The mean error is sufficient to evaluate performance, and standard deviation can be ignored
- C. The high standard deviation suggests that the model's errors are inconsistent; you should apply regularization or tune hyperparameters to reduce variability
- D. Increase the dataset size without making any changes to the model
正解：C
質問 # 70
You are tasked with clustering a large dataset of customer transactions to identify patterns of behavior. You have chosen k-means clustering but are unsure of the optimal number of clusters to use. What steps should you take to determine the optimal number of clusters and ensure high-quality clustering?
Response:
- A. Start with a large number of clusters and manually reduce them over time
- B. Randomly select the number of clusters and observe the clustering performance
- C. Use hierarchical clustering to identify clusters and apply the same number to k-means
- D. Use the elbow method to plot the within-cluster sum of squares and determine the point where the improvement diminishes, and use the silhouette score to evaluate cluster cohesion
正解：D
質問 # 71
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