Certified Machine Learning Associate Practice Exam
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Course
Computers
Institution
Computers
The Certified Machine Learning Associate Practice Exam is designed for entry-level professionals in machine learning. The exam covers machine learning algorithms, data pre-processing, supervised and unsupervised learning, and basic model evaluation. Candidates will be tested on their understanding ...
Certified Machine Learning Associate
Question 1:
What is the primary goal of regression analysis in machine learning?
A. To categorize data into distinct classes.
B. To predict a continuous dependent variable based on one or more independent variables.
C. To reduce the dimensionality of the dataset.
D. To cluster similar data points together.
Answer: B
Explanation:
Regression analysis aims to predict a continuous outcome variable using one or more
predictor variables, distinguishing it from classification (which categorizes data).
Question 2:
Which of the following is NOT a type of regression?
A. Linear Regression
B. Logistic Regression
C. Polynomial Regression
D. K-Means Regression
Answer: D
Explanation:
K-Means is a clustering algorithm, not a regression technique. The other options are all types
of regression methods.
Question 3:
In regression analysis, what is the term for the variable you are trying to predict?
A. Independent Variable
B. Dependent Variable
C. Predictor Variable
D. Feature Variable
Answer: B
Explanation:
The dependent variable is the outcome you're trying to predict, while independent (predictor)
variables are used to make the prediction.
Question 4:
Which assumption is NOT required for Linear Regression?
A. Linearity of relationships
B. Homoscedasticity
C. Normal distribution of errors
D. Categorical dependent variable
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Answer: D
Explanation:
Linear Regression requires the dependent variable to be continuous, not categorical.
Categorical dependent variables are handled by Logistic Regression.
Question 5:
What does R-squared represent in regression analysis?
A. The slope of the regression line
B. The intercept of the regression line
C. The proportion of variance in the dependent variable explained by the independent
variables
D. The correlation coefficient between two variables
Answer: C
Explanation:
R-squared indicates the proportion of the variance in the dependent variable that is
predictable from the independent variables.
Question 6:
Which of the following scenarios is best suited for regression analysis?
A. Predicting whether an email is spam or not.
B. Grouping customers based on purchasing behavior.
C. Estimating the price of a house based on its features.
D. Identifying topics in a set of documents.
Answer: C
Explanation:
Estimating the price of a house involves predicting a continuous variable, making it suitable
for regression analysis.
Question 7:
Which metric is most appropriate for evaluating the accuracy of a regression model?
A. Accuracy
B. Precision
C. Mean Squared Error
D. F1 Score
Answer: C
Explanation:
Mean Squared Error (MSE) measures the average squared difference between predicted and
actual values, making it suitable for regression evaluation.
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,Certified Machine Learning Associate
Question 8:
What is the main difference between simple and multiple linear regression?
A. Simple regression has one dependent variable, multiple has more.
B. Simple regression uses one independent variable, multiple uses two or more.
C. Simple regression uses categorical variables, multiple uses continuous.
D. There is no difference.
Answer: B
Explanation:
Simple linear regression involves one independent variable, while multiple linear regression
involves two or more independent variables.
Question 9:
Which of the following is a potential problem in regression analysis that occurs when
independent variables are highly correlated?
A. Heteroscedasticity
B. Multicollinearity
C. Autocorrelation
D. Overfitting
Answer: B
Explanation:
Multicollinearity refers to the situation where independent variables are highly correlated,
which can destabilize coefficient estimates.
Question 10:
In the context of regression, what does the intercept represent?
A. The value of the dependent variable when all independent variables are zero.
B. The slope of the regression line.
C. The average value of the dependent variable.
D. The point where two variables intersect.
Answer: A
Explanation:
The intercept is the expected value of the dependent variable when all independent variables
are set to zero.
Question 11:
Which of the following is NOT a common application of regression analysis?
A. Predicting stock prices
B. Diagnosing diseases
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C. Estimating real estate values
D. Forecasting sales
Answer: B
Explanation:
While regression can be used in medical studies, diagnosing diseases typically involves
classification rather than regression.
Question 12:
What is the purpose of residuals in regression analysis?
A. To measure the total variation in the dependent variable.
B. To assess the fit of the regression model by examining the differences between observed
and predicted values.
C. To determine the slope of the regression line.
D. To identify the independent variables.
Answer: B
Explanation:
Residuals are the differences between observed and predicted values, used to assess how well
the model fits the data.
Question 13:
Which method is commonly used to estimate the parameters in a linear regression
model?
A. Gradient Descent
B. Maximum Likelihood Estimation
C. Ordinary Least Squares
D. Bayesian Inference
Answer: C
Explanation:
Ordinary Least Squares (OLS) is the most common method for estimating the parameters in a
linear regression model by minimizing the sum of squared residuals.
Question 14:
What does homoscedasticity imply in a regression model?
A. The residuals have constant variance across all levels of the independent variables.
B. The residuals are normally distributed.
C. The residuals are independent of each other.
D. The residuals have increasing variance with the independent variable.
Answer: A
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