MLS-C01 Exploratory Data Analysis Practice Test 18 — 15 Questions

Question 1

A data scientist is exploring a dataset and wants to identify outliers in a numerical feature. The feature is not normally distributed. Which technique is robust to non-normal distributions?

Accepted Answer

Use the IQR method: flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.. The IQR method, because it does not assume a normal distribution and uses quartiles to identify outliers. Option A (MAD) is robust but compares deviations from the median; however, the IQR method is more commonly used for non-normal data. Option C (Z-score) assumes normality. Option D (flagging values more than 3 standard deviations from the mean) also assumes normality.

Answer

Compute the Median Absolute Deviation (MAD) and flag values with MAD > 3.

Answer

Calculate the Z-score and flag values with |Z| > 3.

Answer

Flag values more than 3 standard deviations from the mean.

Question 2

A data scientist is performing EDA on a time series dataset of daily website visits. The scientist wants to identify any seasonality patterns. Which visualization is most appropriate?

Accepted Answer

Line plot with day on x-axis and visits on y-axis, highlighting weekends.. A line plot with day on the x-axis and visits on the y-axis, with weekends highlighted, can reveal weekly seasonality patterns. Option A (correlation matrix with lags) can detect autocorrelation but is not a direct visualization of seasonality. Option B (scatter plot vs day of month) could show monthly patterns but is less effective for daily seasonality and does not preserve time order as clearly as a line plot. Option C (histogram) shows distribution, not temporal patterns. Therefore, option D is best.

Answer

Correlation matrix of visits with lagged versions of itself.

Answer

Scatter plot of visits against the day of the month.

Answer

Histogram of daily visit counts.

Question 3

Refer to the exhibit. A data scientist queries the table with 'SELECT COUNT(*) FROM mytable' in Athena and gets a result of 1000 rows. However, the scientist knows there are 1500 data files in the S3 location. What is the most likely reason for the discrepancy?

Accepted Answer

Some files may be empty or contain only headers, so they contribute 0 rows.. Athena counts rows from data files; if files are empty or contain only headers, they contribute 0 rows. With 1500 files and only 1000 rows, it is plausible that many files are empty or header-only, especially if the data pipeline produces such files. Option A is incorrect because Athena still parses lines even with a delimiter mismatch, treating each line as a row (though columns may be incorrect). Option B is incorrect because schema mismatch typically causes query errors, not silent skipping. Option D is false because Athena does not skip files based on size limits.

Answer

Some files may use a different delimiter (e.g., tab) and are not parsed correctly, resulting in zero rows from those files.

Answer

The table schema does not match the data, causing some files to be skipped.

Answer

Athena skips files larger than a certain size to prevent scanning too much data.

Question 4

A data scientist is exploring a dataset with many features and wants to detect multicollinearity. Which technique should the scientist use?

Accepted Answer

Calculate the Variance Inflation Factor (VIF) for each feature.. Variance Inflation Factor (VIF) is a standard metric for detecting multicollinearity. Option D (pairwise scatter plots) can hint at relationships but does not quantify multicollinearity. Option B (Pearson correlation matrix) shows pairwise linear correlation but does not capture multicollinearity among multiple variables. Option C (ANOVA) is used for comparing means, not for detecting multicollinearity.

Answer

Compute the Pearson correlation matrix between features.

Answer

Perform ANOVA on each feature against the target.

Answer

Create pairwise scatter plots of all features.

Question 5

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains customer demographic information and purchase history. Which approach should the data scientist take to handle missing values without introducing bias into the dataset?

Accepted Answer

Use multiple imputation to estimate missing values.. Multiple imputation produces multiple estimates of missing values, accounting for the uncertainty in the imputation and reducing bias compared to simpler methods. Option A is wrong because dropping all rows with missing values can lead to loss of data and potential bias if missingness is not completely random. Option B is wrong because mean imputation can underestimate variance and distort relationships between variables. Option C is wrong because replacing missing values with a constant (e.g., 0) introduces arbitrary values that can skew the data distribution.

Answer

Drop all rows with any missing values.

Answer

Impute missing values with the mean of each column.

Answer

Replace missing values with a constant, such as 0.

Question 6

A machine learning engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 using AWS Glue. The dataset contains a mix of numeric and categorical features. The engineer wants to efficiently compute summary statistics (e.g., mean, median, standard deviation) for the numeric columns. Which AWS service or feature should the engineer use to achieve this with minimal setup?

Accepted Answer

Use AWS Glue DataBrew to profile the dataset.. AWS Glue DataBrew provides a visual interface to profile data and compute summary statistics without writing code. Option A is wrong because launching an Amazon EMR cluster requires setup and management, which is not minimal. Option C is wrong because Amazon Athena requires writing SQL queries and does not automatically compute summary statistics. Option D is wrong because Amazon SageMaker Data Wrangler is a good tool but requires more configuration than DataBrew for simple summary statistics.

Answer

Launch an Amazon EMR cluster and use Spark.

Answer

Use Amazon Athena to run SQL queries on the data.

Answer

Use Amazon SageMaker Data Wrangler.

Question 7

A data analyst is exploring a dataset with a target variable that is highly imbalanced. The minority class represents only 1% of the data. Which technique should the analyst use to better understand the relationships between features and the minority class?

Accepted Answer

Use stratified sampling to create a balanced sample for analysis.. Stratified sampling ensures the minority class is proportionally represented in the sample, allowing meaningful analysis. Option A is wrong because SMOTE generates synthetic data, which is not appropriate for initial exploratory analysis. Option B is wrong because random sampling may miss the minority class entirely. Option C is wrong because scaling features does not address class imbalance.

Answer

Apply SMOTE to the dataset before analysis.

Answer

Use random sampling to reduce the dataset size.

Answer

Scale the features using Min-Max scaling.

Question 8

A data scientist is analyzing a time series dataset of daily website traffic. The scientist notices a strong weekly seasonality. To better understand the underlying patterns, which decomposition method should the scientist use to separate the trend, seasonal, and residual components?

Accepted Answer

Use STL (Seasonal and Trend decomposition using Loess).. STL (Seasonal and Trend decomposition using Loess) is a robust method for decomposing time series into trend, seasonal, and residual components. It can handle any seasonality period, including weekly seasonality in daily data, and is robust to outliers. Option A is wrong because additive decomposition using moving averages assumes fixed seasonal amplitude and is sensitive to outliers. Option C is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing framework for forecasting, not primarily for decomposition.

Answer

Additive decomposition using moving averages.

Answer

Fit an ARIMA model and examine residuals.

Answer

Apply an ETS (Error, Trend, Seasonal) model.

Question 9

A machine learning team is working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?

Accepted Answer

Truncated Singular Value Decomposition (SVD).. Truncated SVD (e.g., using sklearn's TruncatedSVD) is specifically designed for sparse matrices and efficiently reduces dimensionality while preserving the structure of the sparse matrix. Option A (t-SNE) is primarily for visualization and does not preserve the global structure well for dimensionality reduction. Option C (LDA) is a supervised method that requires labels and is not suitable for unsupervised dimensionality reduction. Option D (PCA using covariance matrix) requires a dense matrix and is computationally expensive for high-dimensional sparse data.

Answer

t-distributed Stochastic Neighbor Embedding (t-SNE).

Answer

Linear Discriminant Analysis (LDA).

Answer

Principal Component Analysis (PCA) using the covariance matrix.

Question 10

A data analyst is examining the distribution of a continuous variable and notices that its histogram is heavily skewed to the right. Which transformation should the analyst apply to make the distribution more symmetrical?

Accepted Answer

Logarithmic transformation (log).. Logarithmic transformation compresses the long tail of right-skewed data, making the distribution more symmetrical. Option A (Box-Cox with lambda=2) is actually a square transformation, which would exacerbate right skewness. Option C (standardization) only centers and scales the data without altering the shape. Option D (square root) can reduce moderate right skew but is less effective than log for severe skewness.

Answer

Box-Cox transformation with lambda=2.

Answer

Standardization (z-score).

Answer

Square root transformation.

Question 11

A data scientist is performing exploratory data analysis on a dataset with both numerical and categorical features. The scientist wants to visualize the pairwise relationships between numerical features and also see the distribution of each feature. Which type of plot should the scientist use?

Accepted Answer

Pair plot (scatter matrix) with histograms on the diagonal.. A pair plot (scatter matrix) shows pairwise scatter plots for numerical features and histograms on the diagonal for distribution of each feature. Option B is incorrect because a box plot shows distribution of a single feature, not pairwise relationships. Option C is incorrect because a heatmap of the correlation matrix shows only correlation values, not distributions or actual data points. Option D is incorrect because a correlation matrix with numbers only shows correlation coefficients, not individual feature distributions or pairwise scatter patterns.

Answer

Box plot for each feature.

Answer

Heatmap of the correlation matrix.

Answer

Correlation matrix with numbers.

Question 12

A team is analyzing a dataset with many categorical features that have high cardinality (e.g., ZIP code, user ID). They want to explore relationships between these features and a continuous target variable. Which approach is most appropriate for visualizing these relationships without overwhelming the viewer?

Accepted Answer

Group categories into top K levels and use a box plot for each group.. When dealing with high-cardinality categorical features, grouping the most frequent categories into a manageable number (e.g., top 10) and using box plots per group allows for clear visualization of the relationship with a continuous target. This approach reduces clutter and highlights differences in distributions. Option B is incorrect because Pearson correlation is designed for continuous variables, not categorical ones. Option C is incorrect: a scatter plot with color-coded categories becomes unreadable with many categories and does not effectively show distributional differences. Option D is incorrect because chi-square statistics assess association between two categorical variables, not between a categorical and a continuous variable.

Answer

Compute a correlation matrix using Pearson correlation.

Answer

Create a scatter plot with each category as a different color.

Answer

Use a heatmap to show pairwise chi-square statistics.

Question 13

A data analyst is investigating a dataset where the target variable is binary (0/1). The analyst wants to check for multicollinearity among the numerical features. Which statistical measure should the analyst use?

Accepted Answer

Variance Inflation Factor (VIF).. Variance Inflation Factor (VIF) is the correct measure to check for multicollinearity among numerical features. VIF quantifies how much a feature is correlated with other features by calculating the ratio of variance of a model with multiple features to variance of a model with one feature. Option B (Mutual information) measures dependence between features and target, not among features. Option C (Chi-square test) is for categorical variables. Option D (Pearson correlation) only measures pairwise linear relationships, not multicollinearity involving multiple features.

Answer

Mutual information between features and target.

Answer

Chi-square test of independence.

Answer

Pearson correlation coefficient between each pair of features.

Question 14

A data scientist is exploring a dataset and wants to identify outliers in a numerical feature. The feature is not normally distributed. Which technique is robust to non-normal distributions?

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

Accepted Answer

Compute Variance Inflation Factor (VIF) for each feature and remove features with VIF > 10.. Options C and E are correct. Variance Inflation Factor (VIF) is a standard metric to detect multicollinearity; removing features with VIF > 10 reduces multicollinearity. Ridge regression (L2 regularization) can also handle multicollinearity by shrinking coefficients, which stabilizes estimates even when predictors are correlated. Option A is incorrect because PCA reduces dimensionality but the resulting components are orthogonal, not the original features, and using all components does not address multicollinearity among the original predictors. Option B is incorrect because standardizing features only changes their scale, not their correlations. Option D is incorrect because stepwise selection does not directly detect or mitigate multicollinearity; it can even produce unstable models if collinearity is present.

Answer

Apply Principal Component Analysis (PCA) and use all components.

Answer

Standardize all features to have zero mean and unit variance.

Answer

Use stepwise feature selection.

Question 15

A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?

Accepted Answer

Add polynomial features (e.g., x^2, x^3) and check if model performance improves.. Options C, D, and E are correct. Adding polynomial features (e.g., x^2, x^3) allows a linear model to capture non-linear relationships. Decision tree regressors naturally model non-linear interactions between predictors and the target. A scatter plot with a LOESS smooth curve visually reveals non-linear patterns in the data. Option A (logistic regression) is incorrect because it is for binary classification, not for exploring non-linearity with a continuous target. Option B (Pearson correlation) only measures linear relationships, so it is not suitable for detecting non-linearity.

Answer

Apply logistic regression to binarize the target.

Answer

Compute the Pearson correlation coefficient between the predictor and target.