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Free Professional Machine Learning Engineer: Professional Machine Learning Engineer Exam Questions and Answers
Last updated: May 11, 2025
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140-Professional Machine Learning Engineer: Professional Machine Learning Engineer
#Question
177. What does "cross-entropy loss" measure in classification tasks?
A.
The average error in predictions
B.
The robustness of the classifier
C.
The gap between predicted and actual probability distributions
D.
The similarity of predictions
Correct Answer:
C
Cross-entropy loss measures the performance of a classification model whose output is a probability value between 0 and 1, quantifying the difference between predicted and actual distributions.
#Question
176. What role does the learning rate play during the training of models?
A.
Increases model complexity
B.
Sets the number of epochs
C.
Determines the step size for updates
D.
Adjusts regularization strength
Correct Answer:
C
The learning rate determines the size of the steps taken toward the minimum of the loss function during optimization, impacting convergence speed and stability.
#Question
175. What strategy does "dropout" employ in neural networks?
A.
Prevents overfitting
B.
Reduces learning rate
C.
Enhances backpropagation
D.
Increases epoch count
Correct Answer:
A
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly setting a fraction of input units to zero during training.
#Question
174. Which approach is used to handle categorical variables in machine learning models?
A.
Cross-validation
B.
Principle Component Analysis
C.
Standardization
D.
Nominal Encoding
Correct Answer:
D
Nominal encoding, often referred to as one-hot encoding, transforms categorical variables into a numerical format suitable for modeling.
#Question
173. In clustering, what does "centroid" refer to?
A.
A type of cluster
B.
The central point of a cluster
C.
Outlier point
D.
A data point with maximum distance
Correct Answer:
B
A centroid is the central point of a cluster in clustering algorithms, representing the average position of all points in the cluster in Euclidean space.
#Question
172. Which metric evaluates how well the model distinguishes between two classes?
A.
Area Under ROC Curve (AUC)
B.
F1 Score
C.
Logistic Loss
D.
Mean Absolute Error
Correct Answer:
A
The Area Under ROC Curve (AUC) measures a model's ability to distinguish between positive and negative classes, providing insight into its classification performance.
#Question
171. What does "bias" refer to in the context of machine learning models?
A.
Misinterpretation of data
B.
Error due to model complexity
C.
Inherent error in the model
D.
Variability in predictions
Correct Answer:
C
Bias refers to the inherent error that is introduced by approximating a real-world problem, which can lead to systematic errors in predictions.
140-Professional Machine Learning Engineer: Professional Machine Learning Engineer
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