MLS-C01 Exploratory Data Analysis Practice Test 12 — 15 Questions

Question 1

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. The log transformation (option C) is appropriate when variance increases with the mean, which is common in time series data. It compresses the scale and stabilizes variance. First-order differencing (A) is used to remove trend or seasonality, not to stabilize variance. The Box-Cox transformation (B) can also stabilize variance, but it is a more general family that includes log as a special case; however, log is simpler and often preferred when the data are positive. Min-max scaling (D) rescales to a fixed range but does not address changing variance.

Answer

First-order differencing

Answer

Box-Cox transformation

Answer

Min-max scaling

Question 2

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. Count encoding uses the frequency of each category as its encoded value, which captures information for rare categories without increasing dimensionality. One-hot encoding (C) would create 1,000 columns, leading to high dimensionality and potential overfitting. Target encoding (A) uses the target variable mean, which can cause overfitting especially with rare categories. Label encoding (B) imposes an arbitrary ordinal relationship, which is inappropriate for nominal categorical features.

Answer

Target encoding

Answer

Label encoding

Answer

One-hot encoding

Question 3

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. Removing one of the highly correlated features reduces multicollinearity. Regularization (B) like Ridge can help but does not remove multicollinearity. Scaling (C) does not affect correlation, so it does not address multicollinearity. PCA (D) can reduce multicollinearity by creating uncorrelated components, but it changes interpretability and is not the simplest solution.

Answer

Apply L2 regularization

Answer

Standardize the features

Answer

Apply Principal Component Analysis

Question 4

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. A log transformation (C) is most appropriate for heavily right-skewed continuous data because it compresses the long right tail and can make the distribution approximately normal. Square root (A) is less effective for severe skewness. Exponential (B) would amplify the skewness. Box-Cox with lambda = 0 (D) is equivalent to log, but since log is explicitly given and commonly known, option C is the direct and correct choice.

Answer

Square root transformation

Answer

Exponential transformation

Answer

Box-Cox transformation with lambda = 0

Question 5

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

Answer

Part-of-speech tagging

Answer

Stemming

Answer

Tokenization

Question 6

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

Answer

Cox proportional hazards model

Answer

Logistic regression

Question 7

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

Accepted Answer

Z-score method. Options C and D are correct. The Z-score method identifies outliers by measuring how many standard deviations a data point is from the mean; points with |Z| > 3 are often considered outliers. The Interquartile Range (IQR) method defines outliers as points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. Option A (Cook's distance) is used in regression to identify influential points, not general univariate outlier detection. Option B (Mahalanobis distance) is a multivariate distance measure. Option E (DBSCAN) is a clustering algorithm that can identify outliers in multivariate space, but not specifically for univariate numeric data.

Answer

Cook's distance

Answer

Mahalanobis distance

Answer

DBSCAN clustering

Question 8

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 9

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

Accepted Answer

Faster convergence of gradient descent. 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

Answer

Reduces the need for feature selection

Answer

Improves performance of decision tree algorithms

Question 10

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. Multiple Imputation by Chained Equations (MICE) models each missing value as a function of other variables, preserving relationships and reducing bias. Option B (forward-fill) is unsuitable for non-time-series data and can introduce bias. Option C (deleting rows) reduces sample size and may introduce bias if data is not missing completely at random. Option D (mean/median imputation) distorts distributions and reduces variance, potentially biasing relationships.

Answer

Use forward-fill to propagate the last observed value

Answer

Delete all rows with missing values

Answer

Replace missing values with the mean or median of each column

Question 11

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. Stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because k-fold cross-validation is a model evaluation technique, not a method for splitting data into training and test sets; using it before splitting would not guarantee class balance. Option B is wrong because oversampling should be done after splitting to avoid data leakage and ensure the test set reflects the original distribution. Option C is wrong because a simple random 80/20 split may not preserve the class distribution due to random variation, especially with imbalanced data.

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 12

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. Log transformation is the most appropriate technique to reduce right skewness (long tail) and make the distribution closer to normal. One-hot encoding is used for categorical variables, not for transforming skewed numerical features. Min-max normalization scales features to a range but does not change the shape of the distribution. Standardization (Z-score) centers the data and scales by standard deviation, but also does not reduce skewness.

Answer

Apply one-hot encoding

Answer

Apply min-max normalization

Answer

Apply standardization (Z-score)

Question 13

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. PCA is a linear dimensionality reduction technique that preserves global structure (variance) and is computationally efficient for large datasets. Option A is wrong because t-SNE is non-linear, slower, and focuses on preserving local structure, not global. Option B is wrong because LDA is a supervised technique that requires class labels, and it is not typically used for unsupervised exploration of clusters. Option D is wrong because UMAP is non-linear and can be slower than PCA; while it preserves both local and global structure to some extent, it is not as computationally efficient as PCA for very large datasets.

Answer

t-SNE

Answer

Linear Discriminant Analysis (LDA)

Answer

UMAP

Question 14

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. A strong positive correlation indicates that the two variables are related, but correlation alone does not imply causation. Option A is incorrect because correlation does not necessarily imply a linear relationship; it could be non-linear or monotonic. Option B is incorrect because correlation does not imply causation. Option D is incorrect because correlation does not guarantee accurate prediction; prediction requires a well-fitted model and additional validation.

Answer

The relationship is linear

Answer

One variable causes the other

Answer

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

Question 15

A data scientist is working with a dataset containing 10,000 observations and 100 features. The scientist wants to detect outliers in the dataset. Which method is most appropriate for outlier detection in a high-dimensional space?

Accepted Answer

Use Isolation Forest. Isolation Forest is the most appropriate method for outlier detection in high-dimensional space because it isolates anomalies by randomly splitting features, making it effective for high-dimensional data without assuming any underlying distribution. Option A is wrong because Z-score assumes normality and is univariate, unsuitable for high-dimensional data. Option C is wrong because Mahalanobis distance assumes multivariate normality and can be computationally expensive and sensitive to high dimensionality. Option D is wrong because IQR is univariate and does not capture interactions between features in high-dimensional spaces.

Answer

Use Z-score to identify points beyond 3 standard deviations

Answer

Use Mahalanobis distance

Answer

Use interquartile range (IQR) for each feature