MLS-C01 Exploratory Data Analysis Practice Test 19 — 15 Questions

Question 1

A machine learning engineer is exploring a dataset with 50 features. Some features are highly correlated. Which technique should the engineer use to reduce dimensionality while preserving variance?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original features into a new set of uncorrelated components (principal components) ordered by the amount of variance they explain. By selecting the top components, the engineer can reduce dimensionality while preserving most of the variance in the data, which is exactly the requirement.

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Factor Analysis

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t-Distributed Stochastic Neighbor Embedding (t-SNE)

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Linear Discriminant Analysis (LDA)

Question 2

A data scientist wants to understand the distribution of a categorical feature with 100 unique values. Which visualization is most appropriate?

Accepted Answer

Bar chart. A bar chart is the most appropriate visualization for displaying the distribution of a categorical feature with 100 unique values because it uses discrete bars to represent the frequency or proportion of each category. Unlike a histogram, which requires continuous numeric bins, a bar chart preserves the distinct categories and allows clear comparison of counts across all 100 levels.

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Histogram

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Scatter plot

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Pie chart

Question 3

A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?

Accepted Answer

Log transformation. Log transformation compresses the range of values and reduces the impact of outliers. Standardization (z-score) does not reduce outlier impact. Min-max scaling is sensitive to outliers. Square root transformation is less effective than log for large outliers. Binning loses information.

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Min-max scaling

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Standardization (z-score)

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Binning

Question 4

A data scientist is performing exploratory data analysis on a dataset with mixed data types (numerical, categorical, text). The goal is to identify clusters of similar records. Which technique is most appropriate?

Accepted Answer

K-prototypes clustering. K-prototypes clustering is specifically designed to handle mixed data types (numerical and categorical) by combining K-means for numerical attributes and K-modes for categorical attributes. It uses a distance measure that integrates Euclidean distance for numerical features and Hamming distance for categorical features, making it suitable for datasets with mixed types. The other algorithms are not inherently designed for mixed data types without preprocessing.

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DBSCAN

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Hierarchical clustering

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K-means clustering

Question 5

Which THREE of the following are appropriate data visualization techniques for exploring the relationship between two numerical variables?

Accepted Answer

Scatter plot. Scatter plot, hexbin plot, and pair plot are designed for bivariate numerical relationships. Bar chart is for categorical. Box plot is for numerical vs categorical.

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Box plot

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Bar chart

Question 6

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.

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Dropping features with more than 50% missing values

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Mean imputation for all features

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Listwise deletion (removing rows with missing values)

Question 7

A data scientist is performing exploratory data analysis on a dataset with missing values. The dataset contains a column 'age' with some missing entries. Which technique is most appropriate for imputing missing values in the 'age' column if the data is normally distributed?

Accepted Answer

Replace missing values with the mean of the column.. When a numeric feature like 'age' is approximately normally distributed, the mean is the optimal point estimate for missing values because the normal distribution is symmetric and its central tendency is best captured by the mean. Imputing with the mean preserves the overall distribution shape and does not bias the central tendency, unlike mode or median which are better suited to categorical or skewed data respectively.

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Drop all rows with missing values.

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Replace missing values with the mode of the column.

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Replace missing values with the median of the column.

Question 8

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 called mean encoding) replaces each category with the average of the target variable for that category, which is a compact and effective way to handle high-cardinality categorical features without exploding dimensionality. It captures the relationship between the category and the target while keeping the feature space small. With over 1 million unique values, one-hot encoding would create an unusable number of columns, and label encoding would impose an arbitrary ordinal relationship.

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Apply one-hot encoding.

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Use label encoding to convert categories to integers.

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Drop the high-cardinality feature.

Question 9

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.. A box plot of the continuous feature grouped by the binary target is the most appropriate visualization because it shows the distribution (median, quartiles, outliers) of the continuous feature separately for each target class. This makes it easy to see whether the feature's distribution differs between class 0 and class 1, which is exactly the relationship a data scientist wants to explore before modeling.

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Scatter plot of the feature vs. the target.

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Bar chart of the feature.

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Histogram of the feature.

Question 10

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.

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Standardization (z-score).

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Square transformation.

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Min-max scaling.

Question 11

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 a single 'Other' bucket is the recommended approach because it retains the signal from the 10 dominant cities while collapsing the long tail of 490 sparse categories into one manageable feature. This reduces dimensionality, prevents overfitting on categories with very few samples, and keeps the model from assigning spurious weights to noise. It is a standard feature-engineering technique for high-cardinality categorical variables.

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Apply label encoding to all categories.

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One-hot encode all 500 categories.

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Drop all rows with rare categories.

Question 12

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 shows two distinct peaks in the feature's probability density. This typically indicates that the data is a mixture of two subpopulations, each with its own central tendency. For example, a dataset of heights might show separate peaks for males and females. Recognizing this is crucial because it suggests the feature may be influenced by a hidden categorical variable, and modeling the data as a single distribution could lead to poor performance.

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The feature has missing values.

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The feature contains outliers.

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The feature needs to be standardized.

Question 13

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

Accepted Answer

Z-score method (assuming normal distribution)

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One-hot encoding

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Principal component analysis (PCA)

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t-SNE

Question 14

Which TWO are common steps in exploratory data analysis?

Accepted Answer

Checking for missing values.

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Training a machine learning model.

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Deploying the model to production.

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Tuning hyperparameters.

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.

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F1 score

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Area under the ROC curve

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Cohen's kappa