MLS-C01 Exploratory Data Analysis Practice Test 11 — 15 Questions

Question 1

A data scientist is examining a dataset for a binary classification problem. The target variable has a 1:1000 imbalance. Which technique should be used to assess model performance during exploratory data analysis?

Accepted Answer

Area under the Precision-Recall curve. With a 1:1000 class imbalance, the positive class is extremely rare. The Area Under the Precision-Recall curve (AUPRC) focuses on the performance of the positive class and is sensitive to changes in precision and recall, making it a robust metric for imbalanced datasets. Unlike ROC AUC, which can be overly optimistic when negatives dominate, AUPRC provides a realistic assessment of model performance on the minority class.

Answer

F1 score

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Area under the ROC curve

Answer

Cohen's kappa

Question 2

A data analyst is working with a time series dataset that shows increasing variance over time. To stabilize the variance before modeling, which transformation is most appropriate?

Accepted Answer

Log transformation. Option A is correct because the log transformation is commonly used to stabilize variance when variance increases with the mean. Box-Cox (B) is more general but requires positive data. Differencing (C) is for trend/seasonality, not variance. Min-max scaling (D) does not stabilize variance.

Answer

First-order differencing

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Box-Cox transformation

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Min-max scaling

Question 3

A team is analyzing a dataset with many categorical features. They notice that one feature has 1,000 unique values but a long tail where most values appear only once. Which encoding method is most appropriate to avoid overfitting?

Accepted Answer

Count encoding. Option C is correct because count encoding uses frequency counts, which can capture information for rare categories without creating high dimensionality. One-hot encoding (A) would create 1,000 columns. Target encoding (B) can cause overfitting. Label encoding (D) implies ordinality.

Answer

Target encoding

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Label encoding

Answer

One-hot encoding

Question 4

A data scientist is exploring a dataset and finds that the correlation between two features is 0.95. What should the data scientist do to address multicollinearity before training a linear regression model?

Accepted Answer

Remove one of the two features. Option A is correct because removing one of the highly correlated features reduces multicollinearity. Regularization (B) like Ridge can help but does not remove multicollinearity. PCA (C) changes interpretability. Scaling (D) does not affect correlation.

Answer

Apply L2 regularization

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Standardize the features

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Apply Principal Component Analysis

Question 5

A machine learning engineer is analyzing a dataset and observes that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. Option B is correct because a log transformation compresses the right tail and is effective for right-skewed data. Square root (A) is less effective for heavy skew. Exponential (C) would worsen skew. Box-Cox (D) is a family that includes log, but log is the most common and straightforward.

Answer

Square root transformation

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Exponential transformation

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Box-Cox transformation with lambda = 0

Question 6

A data scientist is performing EDA on a dataset containing text reviews. To understand the most common words, the data scientist generates a word cloud. Which preprocessing step is most important to ensure the word cloud reflects meaningful content?

Accepted Answer

Stop word removal. Option C is correct because removing stop words (common words like 'the', 'and') ensures that the word cloud highlights meaningful words. Stemming (A) may not be necessary for a word cloud. Tokenization (B) is fundamental but not the most critical for meaningfulness. POS tagging (D) is overkill.

Answer

Part-of-speech tagging

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Stemming

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Tokenization

Question 7

A data analyst is exploring a dataset and notices that the target variable has a Poisson distribution. Which type of model is most appropriate for this target?

Accepted Answer

Poisson regression. Poisson regression is the correct choice because it is specifically designed for modeling count data where the target variable follows a Poisson distribution, which is characterized by non-negative integer values and a variance equal to the mean. This aligns directly with the data analyst's observation of a Poisson-distributed target, making Poisson regression the most appropriate generalized linear model (GLM) for this scenario.

Answer

Linear regression

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Cox proportional hazards model

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Logistic regression

Question 8

Which TWO techniques are appropriate for detecting outliers in a univariate numeric dataset?

Accepted Answer

Z-score method. Options A and C are correct: Z-score identifies outliers based on standard deviations from the mean; IQR uses the interquartile range. DBSCAN (B) is for multivariate clustering. Mahalanobis distance (D) is multivariate. Cook's distance (E) is for regression influence.

Answer

Cook's distance

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Mahalanobis distance

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DBSCAN clustering

Question 9

Which THREE of the following are common causes of multicollinearity in a linear regression model?

Accepted Answer

Including a polynomial term (e.g., x^2) along with the original variable. Options A, C, and D are correct. Dummy variable trap occurs when all categories are included without dropping one. Highly correlated predictors directly cause multicollinearity. Including polynomial terms creates correlation with the original variable. B (interaction terms) can also cause but is less common. E (outliers) does not cause multicollinearity.

Answer

Including interaction terms between independent variables

Answer

Presence of outliers in the target variable

Question 10

Which TWO of the following are benefits of feature scaling for machine learning algorithms?

Accepted Answer

Faster convergence of gradient descent. Option D is correct because feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.

Answer

Eliminates the effect of outliers

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Reduces the need for feature selection

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Improves performance of decision tree algorithms

Question 11

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains both numerical and categorical features. Which approach should the data scientist use to handle missing values while minimizing bias and preserving relationships in the data?

Accepted Answer

Use multiple imputation (e.g., MICE) to impute missing values. Option B is correct because MICE (Multiple Imputation by Chained Equations) models each variable with missing values as a function of other variables, preserving relationships. Option A is wrong because listwise deletion can introduce bias. Option C is wrong because mean/median imputation reduces variance. Option D is wrong because forward-fill is for time series.

Answer

Use forward-fill to propagate the last observed value

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Delete all rows with missing values

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Replace missing values with the mean or median of each column

Question 12

A data engineer is preparing a dataset for training a binary classification model. The target variable is highly imbalanced (95% negative, 5% positive). The engineer needs to split the data into training and test sets while maintaining the class distribution in both sets. Which method should the engineer use?

Accepted Answer

Use stratified random sampling to split the data. Option D is correct because stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because simple random sampling may result in uneven distribution. Option B is wrong because oversampling should be done after splitting to avoid data leakage. Option C is wrong because k-fold cross-validation is not a split method.

Answer

Use k-fold cross-validation and then split the data

Answer

Oversample the minority class first, then do a random split

Answer

Perform a simple random 80/20 split

Question 13

A machine learning engineer is analyzing feature distributions in a dataset and notices that one feature has a long tail. Which transformation is most appropriate to reduce skewness and make the distribution more normal?

Accepted Answer

Apply a log transformation. Option A is correct because log transformation is commonly used to reduce right skewness. Option B is wrong because min-max scaling does not change distribution shape. Option C is wrong because one-hot encoding is for categorical variables. Option D is wrong because standardization does not reduce skewness.

Answer

Apply one-hot encoding

Answer

Apply min-max normalization

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Apply standardization (Z-score)

Question 14

A data scientist is performing exploratory data analysis on a high-dimensional dataset with 500 features. The scientist wants to visualize the data in 2D to check for clusters. Which dimensionality reduction technique should the scientist use that preserves global structure and is computationally efficient for large datasets?

Accepted Answer

PCA. Option C is correct because PCA is linear, fast, and preserves global variance structure. Option A is wrong because t-SNE is non-linear, slower, and focuses on local structure. Option B is wrong because UMAP can be slow and is also non-linear. Option D is wrong because LDA is supervised and requires labels.

Answer

t-SNE

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Linear Discriminant Analysis (LDA)

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UMAP

Question 15

A data analyst is examining a scatter plot of two variables and notices a strong positive correlation. Which of the following is a valid conclusion?

Accepted Answer

The two variables are related, but causation cannot be inferred. Option A is correct because correlation indicates a relationship, but does not imply causation. Option B is wrong because correlation does not imply causation. Option C is wrong because correlation does not provide a prediction model. Option D is wrong because correlation does not guarantee linearity; it could be non-linear.

Answer

The relationship is linear

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One variable causes the other

Answer

The relationship can be used to accurately predict one variable from the other