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

Question 1

Which TWO of the following are appropriate methods for handling missing data in a dataset?

Accepted Answer

Multiple imputation. Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions. Dropping features with many missing values may lose information.

Answer

Dropping features with more than 50% missing values

Answer

Mean imputation for all features

Answer

Listwise deletion (removing rows with missing values)

Question 2

A data engineer is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The analysis reveals high cardinality in a categorical feature with over 1 million unique values. What is the best approach to handle this before training a model?

Accepted Answer

Use target encoding based on the mean of the target variable per category.. Target encoding (also known as mean encoding) replaces each category with the mean of the target variable for that category, effectively handling high cardinality without exploding dimensionality or imposing ordinal relationships. Option A is wrong because one-hot encoding on a feature with 1 million unique values would create over 1 million columns, making the dataset sparse and computationally expensive. Option B is wrong because label encoding assigns arbitrary integers, which may introduce unintended ordinal relationships and mislead the model. Option C is wrong because dropping the feature could discard valuable predictive information.

Answer

Apply one-hot encoding.

Answer

Use label encoding to convert categories to integers.

Answer

Drop the high-cardinality feature.

Question 3

During exploratory data analysis, a data scientist observes a strong correlation (r=0.95) between two numeric features. The model to be trained is a linear regression. What is the most appropriate action?

Accepted Answer

Remove one of the correlated features.. High correlation (r=0.95) between two features indicates severe multicollinearity in linear regression, which can cause unstable coefficient estimates and inflated standard errors. The most straightforward solution is to remove one of the correlated features (Option B), as it directly eliminates the redundancy. Option A (standardization) does not affect correlation. Option C (L2 regularization) can help but is not the first choice because removal is simpler and preserves interpretability; regularization only shrinks coefficients but does not remove the linear dependence. Option D (interaction term) would increase multicollinearity, making the problem worse.

Answer

Apply standardization to both features.

Answer

Use L2 regularization (Ridge regression) without removing features.

Answer

Create an interaction term between the two features.

Question 4

A data scientist is analyzing a dataset with a target variable that is binary (0/1). Which visualization is most appropriate to explore the relationship between a continuous feature and the target?

Accepted Answer

Box plot of the feature grouped by target.. The most appropriate visualization to explore the relationship between a continuous feature and a binary target is a box plot of the feature grouped by the target. This allows comparing the distribution of the continuous feature across the two target classes (0 and 1), revealing differences in central tendency, spread, and outliers. Option A (scatter plot) is unsuitable because scatter plots are for two continuous variables, not a binary target. Option B (bar chart) is typically used for categorical features, not continuous ones. Option D (histogram) shows the distribution of a single continuous variable but does not separate by target class, so it cannot reveal the relationship with the binary target. Therefore, option C is correct.

Answer

Scatter plot of the feature vs. the target.

Answer

Bar chart of the feature.

Answer

Histogram of the feature.

Question 5

In exploratory data analysis, a data scientist notices that the distribution of a feature 'income' is heavily right-skewed. Which transformation is most appropriate to reduce skewness?

Accepted Answer

Log transformation.. Log transformation is the most appropriate technique to reduce right skewness in a feature like 'income' because it compresses the long tail of high values while expanding the lower end, making the distribution more symmetric. This is particularly effective for income data, which often follows a log-normal distribution, and is a standard preprocessing step in machine learning to improve model performance.

Answer

Standardization (z-score).

Answer

Square transformation.

Answer

Min-max scaling.

Question 6

A data scientist is exploring a dataset with 1,000 features and only 200 samples. The goal is to build a binary classifier. Which technique should be used first during exploratory data analysis to reduce dimensionality and avoid overfitting?

Accepted Answer

Use principal component analysis (PCA) to reduce dimensions.. PCA is an unsupervised dimensionality reduction technique that is well-suited for high-dimensional datasets with few samples, as it reduces features while retaining variance and helps avoid overfitting. Option A is wrong because pairwise correlation only captures linear relationships and may miss interactions, and removing correlated features may not be sufficient for high dimensionality. Option B is wrong because L1 regularization (Lasso) is a model-based feature selection method applied during training, not during initial exploratory data analysis (EDA). Option C is wrong because t-SNE is a visualization technique for reducing dimensions to 2 or 3 for plotting, but it is not suitable for generating features for modeling and can be unstable with few samples.

Answer

Compute pairwise correlations and remove highly correlated features.

Answer

Apply L1 regularization (Lasso) to select features.

Answer

Use t-SNE to visualize clusters and reduce dimensions.

Question 7

During EDA, a data scientist finds that a categorical feature 'city' has 500 unique values but only 10 cities account for 90% of the data. What is a recommended way to handle the rare categories?

Accepted Answer

Group rare categories into a single 'Other' category.. Grouping rare categories into 'Other' reduces cardinality, avoids overfitting from high-dimensional sparse features, and retains the majority of data from the top 10 cities. Option B (label encoding) is not recommended as it imposes an arbitrary ordinal relationship that may mislead the model. Option C (one-hot encoding all 500 categories) would create 499 dummy features, leading to the curse of dimensionality and sparse data. Option D (dropping rows with rare categories) discards potentially valuable data and may introduce bias.

Answer

Apply label encoding to all categories.

Answer

One-hot encode all 500 categories.

Answer

Drop all rows with rare categories.

Question 8

A data scientist is performing EDA and observes that a feature 'purchase_amount' has many zeros and a long tail of positive values. What type of model would be appropriate for this target variable?

Accepted Answer

Zero-inflated negative binomial regression.. Zero-inflated negative binomial regression models are designed for count data with a high frequency of zeros, which matches the 'purchase_amount' feature having many zeros and a long tail of positive values. Option B is incorrect: Log transformation does not handle the zero-inflation problem; zeros become undefined or need adjustment. Option C is incorrect: Logistic regression is for binary outcomes, not continuous or count data. Option D is incorrect: Poisson regression accommodates count data but assumes the variance equals the mean and does not handle excess zeros; zero-inflation violates this assumption.

Answer

Linear regression after log transformation.

Answer

Logistic regression on binary indicator of purchase.

Answer

Poisson regression.

Question 9

During EDA, a data scientist plots the distribution of a feature and sees a bimodal pattern. What does this likely indicate?

Accepted Answer

The data may contain two distinct groups.. A bimodal distribution has two distinct peaks, which typically indicates that the data contains two different subpopulations or clusters. This is a common finding in exploratory data analysis (EDA) when the feature is influenced by a categorical variable with two categories. For example, in a dataset of customer purchases, transaction amounts may be bimodal if there are two types of customers (e.g., individuals and businesses). Therefore, option A is correct. Option B is incorrect because missing values usually appear as a separate bar or a spike at a specific value, not as a second peak. Option C is incorrect because outliers typically appear as extreme values far from the main distribution, not as a second mode. Option D is incorrect because standardization (scaling to zero mean and unit variance) does not change the shape of the distribution; it only changes the scale.

Answer

The feature has missing values.

Answer

The feature contains outliers.

Answer

The feature needs to be standardized.

Question 10

Which TWO are appropriate techniques for detecting outliers in a dataset during exploratory data analysis?

Accepted Answer

Z-score method (assuming normal distribution)

Answer

One-hot encoding

Answer

Principal component analysis (PCA)

Answer

t-SNE

Question 11

Which THREE are valid reasons to perform feature scaling during exploratory data analysis?

Accepted Answer

To improve performance of distance-based algorithms like KNN.

Answer

To change the shape of the feature distribution.

Answer

To increase the number of features.

Question 12

Which TWO are common steps in exploratory data analysis?

Accepted Answer

Checking for missing values.

Answer

Training a machine learning model.

Answer

Deploying the model to production.

Answer

Tuning hyperparameters.

Question 13

A data scientist is analyzing a dataset with missing values in a numeric column. The missing rate is 30% and the data is not missing completely at random. Which imputation method should the data scientist avoid to minimize bias?

Accepted Answer

Mean imputation. Mean imputation (Option A) should be avoided when data is not missing completely at random (NMAR) because it can introduce bias by underestimating variance and distorting the relationships between variables. Options B (model-based imputation), C (k-NN imputation), and D (multiple imputation) are more robust for non-random missing data as they account for patterns in the data and produce less biased estimates.

Answer

Model-based imputation using linear regression

Answer

k-Nearest Neighbors imputation

Answer

Multiple imputation using chained equations

Question 14

A machine learning engineer is exploring a dataset with 500 features and 10,000 samples. To reduce dimensionality for visualization, which technique is most suitable if the goal is to preserve global data structure?

Accepted Answer

Principal Component Analysis (PCA). PCA is the most suitable technique for preserving the global data structure when reducing dimensionality because it is a linear method that maximizes variance along orthogonal principal components, capturing the overall covariance structure of the 500 features. Unlike nonlinear methods, PCA ensures that the global relationships (e.g., distances between clusters) are retained, making it ideal for visualization of high-dimensional data where the goal is to see broad patterns.

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Locally Linear Embedding (LLE)

Answer

Uniform Manifold Approximation and Projection (UMAP)

Question 15

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

Answer

Area under the ROC curve

Answer

Cohen's kappa