MLS-C01 Exploratory Data Analysis Practice Test 22 — 15 Questions

Question 1

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (only 1% positive class). The goal is to build a binary classifier. During exploratory data analysis, which metric is MOST appropriate to evaluate the performance of different sampling strategies before model training?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Area Under the Receiver Operating Characteristic Curve (AUC ROC). The most appropriate metric during exploratory data analysis for evaluating sampling strategies with imbalanced data is AUC ROC, as it is independent of the class distribution and measures the model's ability to distinguish between positive and negative classes regardless of the threshold. Option A (RMSE) is used for regression tasks, not classification. Option C (F1 score) depends on a specific threshold and can be affected by sampling changes. Option D (Accuracy) is misleading for imbalanced datasets because a high accuracy can be achieved by predicting the majority class.

Answer

Root Mean Squared Error (RMSE)

Answer

F1 score

Answer

Accuracy

Question 2

A data engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?

Accepted Answer

Use multiple imputation to generate several plausible values and combine results. Multiple imputation provides unbiased estimates under MAR by accounting for uncertainty and preserving relationships between variables. Option A is wrong because mean imputation reduces variance, distorts distributions, and can bias estimates. Option B is wrong because listwise deletion reduces sample size and can introduce bias if missingness is related to other variables. Option C is wrong because median imputation is a single imputation method that does not account for the uncertainty due to missing data and may still introduce bias.

Answer

Replace missing values with the mean (45)

Answer

Remove rows with missing 'age' values

Answer

Replace missing values with the median

Question 3

A machine learning engineer is analyzing a dataset that contains a categorical feature 'country' with 200 unique values. The target variable is binary. The engineer wants to use this feature in a linear model. Which encoding method should be applied during EDA to prepare the data for modeling, considering the high cardinality?

Accepted Answer

Target encoding with cross-validation. Target encoding with cross-validation (Option A) is the correct choice for this scenario because it replaces each category in the high-cardinality feature 'country' with the mean of the target variable, effectively capturing the relationship with the target while avoiding the curse of dimensionality. Cross-validation is essential to prevent overfitting by computing the target means on out-of-fold data. One-hot encoding (Option D) would create 199 dummy variables, leading to high dimensionality and potential overfitting, making it unsuitable for linear models with limited data. Label encoding (Option B) imposes an arbitrary ordinal relationship that the linear model would misinterpret. Frequency encoding (Option C) may not capture the relationship with the target and could lose predictive power.

Answer

Label encoding

Answer

Frequency encoding

Answer

One-hot encoding

Question 4

A data analyst is performing exploratory data analysis on a dataset with 100 features. The analyst wants to identify which features contribute most to the variance in the data. Which technique should the analyst use?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is the correct technique because it is an unsupervised dimensionality reduction method that identifies the principal components, which are linear combinations of the original features that capture the maximum variance in the data. Option A (K-means) is incorrect because it is a clustering algorithm that groups data points, not used for analyzing feature variance. Option C (t-SNE) is incorrect because it is primarily used for visualizing high-dimensional data in lower dimensions but does not provide explicit variance contributions. Option D (LDA) is incorrect because it is a supervised method that requires class labels and aims to maximize class separability, not variance.

Answer

K-means clustering

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Question 5

During EDA, a data scientist discovers that a numerical feature 'income' has a skewness of 3.5. Which transformation should the scientist apply to make the distribution more symmetric?

Accepted Answer

Log transformation. A log transformation is commonly applied to right-skewed positive data to reduce skewness and make the distribution more symmetric. Option A is wrong because standardization (Z-score) centers and scales the data but does not change the shape of the distribution. Option B is wrong because a square transformation would increase skewness for right-skewed data. Option D is wrong because min-max scaling rescales the data to a fixed range but does not alter the distribution's skewness.

Answer

Standardization (Z-score)

Answer

Square transformation

Answer

Min-Max scaling

Question 6

A data scientist is exploring a dataset with a large number of features. The scientist suspects that some features are redundant because they are highly correlated with each other. Which technique should the scientist use during EDA to identify and remove such redundant features?

Accepted Answer

Variance Inflation Factor (VIF). Variance Inflation Factor (VIF) quantifies multicollinearity by measuring how much the variance of a coefficient is inflated due to correlation with other features. Features with high VIF (typically >5 or >10) are considered highly correlated and can be removed. Option A is incorrect because chi-square test is used for testing independence between categorical variables, not for identifying redundant features. Option B is incorrect because PCA reduces dimensionality by creating new uncorrelated features, but it does not directly identify which original features are redundant. Option C is incorrect because while a correlation matrix heatmap can show pairwise correlations, it does not account for multicollinearity among multiple features; VIF is more comprehensive.

Answer

Chi-square test

Answer

Principal Component Analysis (PCA)

Answer

Correlation matrix heatmap

Question 7

In exploratory data analysis, a data scientist notices that the distribution of a continuous variable is bimodal. The scientist suspects that the two modes correspond to two different groups in the data. Which visualization is MOST appropriate to confirm this suspicion?

Accepted Answer

Histogram with overlaid densities by group. The most appropriate because a histogram with overlaid densities, colored by group, directly shows the distribution of each group and can reveal whether the two modes correspond to different groups. Option A (box plot) displays summary statistics but not the shape or modality. Option B (bar chart) is for categorical data, not continuous. Option D (scatter plot) is for two continuous variables, not for examining a single distribution.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 8

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (only 1% positive class). The goal is to build a binary classifier. During exploratory data analysis, which metric is MOST appropriate to evaluate the performance of different sampling strategies before model training?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Interquartile range (IQR) with multiplier 1.5. The IQR method (Option A) is the most robust for detecting outliers in the 'transaction_amount' feature because it is based on quartiles and does not assume any underlying distribution, making it resistant to skew and extreme values. Z-score (Option C) is inappropriate because it assumes a normal distribution, which the long right tail violates. Mahalanobis distance (Option B) assumes multivariate normality and is not suited for univariate outlier detection. DBSCAN (Option D) is computationally expensive on 1 million rows and is designed for density-based clustering, not for univariate outlier detection.

Answer

Mahalanobis distance

Answer

Z-score with threshold 3

Answer

DBSCAN clustering

Question 9

A data scientist is performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)

Accepted Answer

Create an indicator column to flag whether the value was missing, then impute with a placeholder. Options C and E are correct. Creating an indicator column to flag missingness and then imputing with a placeholder (e.g., mean/median) is a common technique to preserve information about missing patterns. Imputing numerical columns with median and categorical with mode is robust to outliers and preserves distribution. Option A (forward fill) is typically used for time series data, not general tabular EDA. Option B (listwise deletion) can reduce sample size and introduce bias if data is not missing completely at random. Option D (mean imputation) is sensitive to outliers and can distort variance.

Answer

Use forward fill to propagate the last observed value

Answer

Remove all rows with any missing value (listwise deletion)

Answer

Impute missing values with the mean of each column

Question 10

A data scientist is evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)

Accepted Answer

One-hot encoding. Options A, B, and D are correct: One-hot encoding (A) can be used for ordinal variables, though it ignores order, it is still valid. Target encoding (B) captures the relationship with the target and respects ordinality. Label encoding (D) preserves the ordinal nature. Option C (hash encoding) is incorrect because it is typically used for high-cardinality nominal variables, not ordinal, and may lose interpretability. Option E (binary encoding) is also incorrect because it is designed for nominal categories and does not maintain order.

Answer

Hash encoding (using feature hashing)

Answer

Binary encoding (convert to binary representation)

Question 11

During EDA, a data scientist generates a pairplot of the dataset and observes that two features have a Pearson correlation coefficient of 0.95. Which TWO conclusions can the scientist draw from this observation? (Choose 2)

Accepted Answer

The two features may be multicollinear. Options A and B are correct because a Pearson correlation coefficient of 0.95 indicates a very strong positive linear relationship between the two features. This strong linear relationship suggests potential multicollinearity if both features are used as predictors in a linear model. Option C is incorrect because a positive correlation means the features move in the same direction, not opposite. Option D is wrong because a high correlation implies dependence, not statistical independence. Option E is incorrect because correlation does not imply causation; it only measures the strength and direction of a linear relationship.

Answer

The two features move in opposite directions

Answer

The two features are statistically independent

Answer

One feature causes the other

Question 12

Refer to the exhibit. A data scientist plans to read this CSV file into memory for exploratory data analysis using pandas. The instance has 8 GB of RAM. What is the MOST likely issue the scientist will encounter?

Accepted Answer

The file is too large to load into memory on this instance. The file size is approximately 1 GB (1073741824 bytes = 1 GB), and pandas typically requires 3-5x the file size in memory for CSV parsing, which would exceed the 8 GB RAM. Option A is wrong because pandas can handle 10 million rows; the issue is memory, not row count. Option C is wrong because the ContentType is text/csv, so it is indeed CSV format. Option D is wrong because there is no indication of permission issues (HTTP 200).

Answer

The file contains too many rows for pandas to handle

Answer

The file is not in CSV format despite the ContentType

Answer

The file is not accessible because of insufficient permissions

Question 13

Refer to the exhibit. A data scientist examines a sample of data and notices that all columns are numeric. The scientist wants to check for multicollinearity. Which statistic should be computed from this sample?

Accepted Answer

Correlation matrix (Pearson). The correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because chi-square is for categorical variables, not numeric. Option C is wrong because VIF requires more variables than observations (or typically used after regression). Option D is wrong because covariance alone is scale-dependent.

Answer

Chi-square test of independence

Answer

Variance Inflation Factor (VIF)

Answer

Covariance matrix

Question 14

A team is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The dataset contains missing values, outliers, and categorical variables with high cardinality. The team wants to understand data distributions and relationships before modeling. Which combination of Data Wrangler features should they use?

Accepted Answer

Generate a data quality report, view histograms, and create scatter plots for selected features.. SageMaker Data Wrangler provides built-in features for exploratory data analysis, including data quality reports (with summary statistics and missing value analysis), histograms for distribution visualization, and scatter plots to explore relationships between features. These are ideal for understanding distributions and correlations early in the pipeline. Option B is incorrect because dropping rows is a data cleaning transformation, not an EDA step, and box plots alone are insufficient for understanding relationships. Option C is incorrect because imputation and one-hot encoding are data preparation transformations applied after EDA. Option D is incorrect because while Data Wrangler generates a data quality report, it does not directly include correlation heatmaps; scatter plots (as in A) are a more direct way to assess relationships.

Answer

Drop rows with missing values and visualize box plots for numerical features.

Answer

Use imputation to handle missing values and one-hot encoding for categorical features.

Answer

Generate a data quality report and a correlation heatmap.

Question 15

A machine learning engineer notices that the target variable in a regression dataset has a long-tailed distribution. Which visualization technique is most appropriate to assess the distribution before applying a log transformation?

Accepted Answer

Histogram with density curve. (Histogram with density curve) is the most appropriate visualization for assessing a long-tailed distribution because it clearly shows the shape, spread, and tail behavior of the target variable. A histogram with an overlaid density curve helps identify skewness and the need for a log transformation. Option A (Bar chart) is for categorical data, not continuous distributions. Option C (Box plot) provides quartiles and outliers but does not fully reveal the distribution shape, especially the length of tails. Option D (Scatter plot) visualizes relationships between two variables, not a univariate distribution.

Answer

Bar chart

Answer

Box plot

Answer

Scatter plot