MLS-C01 Exploratory Data Analysis Practice Test 24 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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 Singular Value Decomposition (SVD) is specifically designed for sparse matrices and can reduce dimensionality without densifying the data. It computes the top k singular values and vectors, preserving the sparsity structure and making it efficient for high-dimensional sparse features like bag-of-words.

Answer

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

Answer

Linear Discriminant Analysis (LDA).

Answer

Principal Component Analysis (PCA) using the covariance matrix.

Question 6

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).. A logarithmic transformation compresses the right tail of a right-skewed distribution, pulling in large values and making the distribution more symmetrical and closer to normal. It is the standard remedy for positive right skew, especially when the data spans orders of magnitude. Box-Cox is a family that includes log (lambda=0), but lambda=2 is a square transformation that would worsen right skew, not fix it.

Answer

Box-Cox transformation with lambda=2.

Answer

Standardization (z-score).

Answer

Square root transformation.

Question 7

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) plots pairwise scatterplots for numerical features and can place histograms (or KDEs) on the diagonal to show each feature's distribution. This single visualization satisfies both requirements: pairwise relationships between numerical features and univariate distributions. Box plots, heatmaps, and correlation matrices each address only part of the requirement.

Answer

Box plot for each feature.

Answer

Heatmap of the correlation matrix.

Answer

Correlation matrix with numbers.

Question 8

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.. Grouping high-cardinality categorical features into top K levels and using box plots for each group is the most appropriate approach. This reduces the number of categories to a manageable number, allowing clear visualization of the distribution of the continuous target variable across the most frequent categories. It avoids overwhelming the viewer with too many categories while still revealing relationships.

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 9

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 standard statistical measure for detecting multicollinearity among numerical features. VIF quantifies how much the variance of a regression coefficient is inflated due to correlation with other predictors; values above 5 or 10 typically indicate problematic multicollinearity. It directly addresses the analyst's goal of checking relationships among the numerical features themselves.

Answer

Mutual information between features and target.

Answer

Chi-square test of independence.

Answer

Pearson correlation coefficient between each pair of features.

Question 10

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

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.. Option C is correct because the Variance Inflation Factor quantifies how much the variance of an estimated regression coefficient is inflated due to collinearity with other predictors, and a common rule of thumb is that VIF > 10 (or sometimes > 5) indicates problematic multicollinearity that warrants removing or combining the offending features. Option E is correct because Ridge regression adds an L2 penalty (lambda * sum of squared coefficients) to the ordinary least squares loss, which shrinks correlated coefficients toward zero and stabilizes estimates, making it a standard way to handle multicollinearity without dropping features. Option A is not appropriate as stated because although PCA can decorrelate features, using all components retains the same dimensionality and does not by itself address multicollinearity for interpretable regression; typically one drops components or uses PCR. Option B is a preprocessing step for scale sensitivity and does not detect or resolve correlation among features. Option D, stepwise selection, may incidentally drop some correlated variables but is not a reliable or targeted multicollinearity detection/handling method and can introduce other issues such as overfitting and unstable selection.

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 11

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.

Question 12

A data analyst is performing exploratory data analysis on a dataset and notices that there are outliers in several numerical columns. Which TWO methods can the analyst use to identify outliers?

Accepted Answer

Calculate z-scores and flag any data points with |z| > 3.. Option B is correct because calculating z-scores standardizes each value relative to the column's mean and standard deviation, and flagging points with |z| > 3 is a standard statistical rule for identifying extreme outliers in numerical data. Option C is correct because a box plot displays the interquartile range and whiskers (typically 1.5×IQR beyond Q1 and Q3), so points plotted beyond the whiskers are explicitly identified as outliers. Option A is not a reliable outlier-identification method here, since a scatter plot matrix shows pairwise relationships and may not clearly reveal univariate outliers in several numerical columns. Option D does not identify outliers; comparing mean and median only indicates skewness or asymmetry in a distribution. Option E is also insufficient, as histogram gaps may reflect binning choices or multimodal distributions rather than true outliers.

Answer

Create a scatter plot matrix to visually inspect.

Answer

Compare the mean and median of each column.

Answer

Plot a histogram and look for gaps.

Question 13

A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?

Accepted Answer

The first query uses a partition column (year), allowing partition pruning, while the second query does not.. The table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions, resulting in a longer execution time. Option A is incorrect because compression does not directly affect partition pruning; it reduces storage size but not scan time in this context. Option C is incorrect because the data format (Parquet) could help with columnar pruning, but the key difference here is partition pruning, not file format. Option D is incorrect because using SELECT * does not inherently cause slow performance; the lack of partition pruning is the main issue.

Answer

The data is compressed, and the first query benefits from compression.

Answer

The data is stored in Parquet format, which is optimized for columnar access.

Answer

The second query is not optimized because it uses 'SELECT *'.

Question 14

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler is purpose-built for data preparation and exploratory analysis, including correlation matrices, feature-target correlation, and feature importance visualizations. It integrates directly with SageMaker Studio and can export the analysis into a processing job or pipeline, making it the most appropriate service for identifying features correlated with a target variable.

Answer

Amazon QuickSight

Answer

Amazon Athena

Answer

AWS Glue DataBrew

Question 15

A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?

Accepted Answer

Use Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.. Amazon SageMaker Data Wrangler is designed for exploratory data analysis (EDA) and can quickly generate visualizations like histograms. By grouping histograms by the class label, the data scientist can directly compare the distribution of each numeric feature for class A versus class B, which is exactly what is needed to understand feature distributions in an imbalanced dataset. This approach is efficient, requires minimal code, and integrates seamlessly with SageMaker workflows.

Answer

Run a t-test for each feature to determine statistical significance between classes.

Answer

Generate box plots for each feature using Amazon QuickSight.

Answer

Compute the correlation matrix between features and the target.