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GIAC Machine Learning Engineer Sample Questions (Q143-Q148):
NEW QUESTION # 143
Which of the following are common supervised learning algorithms?
(Choose two)
Response:
- A. Linear Regression
- B. DBSCAN
- C. k-means
- D. Random Forest
Answer: A,D
NEW QUESTION # 144
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 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
- B. The model is underfitting the data; increasing the number of features and trees will improve performance
- C. The features are not useful; removing all but one feature will reduce overfitting
- D. The training data is too large; reducing the training set size will improve the model's performance
Answer: A
NEW QUESTION # 145
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. Use hierarchical clustering to identify clusters and apply the same number to k-means
- B. Randomly select the number of clusters and observe the clustering performance
- C. 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
- D. Start with a large number of clusters and manually reduce them over time
Answer: C
NEW QUESTION # 146
In machine learning, what is 'data normalization'?
Response:
- A. Dividing the dataset into training and test sets
- B. Encoding categorical variables into numerical values
- C. Scaling numeric features to a standard range
- D. Removing duplicate records from the dataset
Answer: C
NEW QUESTION # 147
Unsupervised learning is primarily used for:
Response:
- A. Finding hidden patterns in unlabeled data
- B. Classification tasks with predefined categories
- C. Regression analysis with continuous output
- D. Predicting outcomes based on labeled data
Answer: A
NEW QUESTION # 148
......
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