MLS-C01 Exploratory Data Analysis Practice Test 1 — 15 Questions

Question 1

A data scientist is exploring a dataset of customer transactions. The dataset has 1 million rows and 50 columns. The target variable is a binary flag indicating whether a customer churned. The data scientist runs a correlation matrix on all numerical features and finds that two features have a correlation coefficient of 0.98. Which action should be taken to improve model performance?

Accepted Answer

Remove one of the two highly correlated features from the dataset.. Two features with a correlation coefficient of 0.98 are nearly perfectly multicollinear. This inflates the variance of coefficient estimates in linear models, making them unstable and reducing interpretability. Removing one of the highly correlated features is a standard dimensionality reduction technique that mitigates multicollinearity without significant information loss, as the remaining feature captures almost the same variance.

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Create an interaction term between the two features.

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Increase the regularization parameter (e.g., lambda) in the model.

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Apply mean-centering to both features to reduce correlation.

Question 2

A team is building a regression model to predict house prices. The dataset includes a column 'zip_code' with 100 unique values. The data scientist one-hot encodes this column, resulting in 100 new binary columns. The model shows poor performance on a validation set. What is the most likely cause?

Accepted Answer

The model suffers from the curse of dimensionality due to the large number of features.. One-hot encoding 'zip_code' with 100 unique values creates 100 binary features. With only 100 features, the dataset is not high-dimensional enough to cause the curse of dimensionality, which typically requires thousands of features. The poor performance is more likely due to other issues like overfitting or data leakage, not the curse of dimensionality. Option D is incorrect because the curse of dimensionality is not the most likely cause in this scenario.

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One-hot encoding introduced multicollinearity among the binary columns.

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One-hot encoding reduced the number of features, causing underfitting.

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The one-hot encoding introduced high variance, but the validation set has low variance.

Question 3

During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?

Accepted Answer

Apply a log transformation to the feature.. A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.

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Apply z-score normalization.

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Apply one-hot encoding.

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Apply min-max scaling.

Question 4

A data scientist is analyzing a dataset with missing values in 30% of the rows for the 'age' column. The data scientist decides to impute the missing values with the median of the observed 'age' values. What is a potential drawback of this approach?

Accepted Answer

The imputed values may reduce the variance of the 'age' distribution.. Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.

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The imputation will introduce bias if the missing values are not random.

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Imputation using median is computationally expensive for large datasets.

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The imputed values will increase the variance of the feature, leading to overfitting.

Question 5

A data scientist is exploring a dataset with 500 features and 10,000 samples. The data scientist computes the pairwise correlation matrix and finds that many features have correlations above 0.9. The data scientist wants to reduce the dataset to 50 features while preserving as much variance as possible. Which technique should be used?

Accepted Answer

Apply Principal Component Analysis (PCA) and keep the top 50 principal components.. Principal Component Analysis (PCA) is the correct technique because it performs an orthogonal linear transformation that projects the original 500 features into a new coordinate system where the axes (principal components) are ordered by the variance they capture. By keeping the top 50 principal components, the data scientist retains the maximum possible variance in the reduced 50-dimensional space, directly addressing the goal of preserving variance while handling high multicollinearity.

Answer

Remove all but one feature from each group of highly correlated features.

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Use Linear Discriminant Analysis (LDA) to project to 50 dimensions.

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Use t-Distributed Stochastic Neighbor Embedding (t-SNE) to reduce to 50 dimensions.

Question 6

A data scientist is performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)

Accepted Answer

Use multiple imputation to fill in the missing values.. Option C is correct because multiple imputation is a robust statistical technique that accounts for uncertainty in missing values by creating multiple complete datasets, analyzing each, and pooling results. This is particularly appropriate for a dataset with 10,000 rows and 20 features, as it preserves the sample size and avoids bias that simpler methods might introduce.

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Replace missing values with the mode of the feature.

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Identify and remove outliers from the feature.

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Drop the entire feature from the dataset.

Question 7

A data scientist is analyzing a dataset of customer reviews. The dataset contains a text column 'review' and a numerical rating from 1 to 5. The data scientist wants to create features for sentiment analysis. Which THREE preprocessing steps should be applied to the text data before feature extraction? (Choose THREE.)

Accepted Answer

Apply stemming to reduce words to their root form.. Option B is correct because stemming reduces words to their root form (e.g., 'running' to 'run'), which consolidates variations of the same word and reduces feature dimensionality. This is a standard preprocessing step before feature extraction in NLP tasks like sentiment analysis, as it helps the model generalize across different word forms.

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Standardize the text data using z-score normalization.

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Tokenize the text into individual words.

Question 8

A data scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?

Accepted Answer

Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.. Option C is correct because stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.

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Randomly sample 10% of the data and plot feature distributions by class.

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Apply PCA to reduce dimensionality, then visualize the first two components.

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Focus only on the majority class features to avoid bias.

Question 9

During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?

Accepted Answer

Create a binary indicator for missingness and impute the continuous values with the median.. Option B is correct because it preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.

Answer

Remove the feature entirely to avoid bias.

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Impute missing values with -1 since it is out of range.

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Drop all rows with missing values in that feature.

Question 10

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target variable is binary. After checking for multicollinearity, the scientist finds many pairs of features with correlation > 0.95. Which action should be taken to prepare the data for modeling?

Accepted Answer

For each highly correlated pair, remove one feature based on domain knowledge or higher correlation with target.. Option C is correct because when features are highly correlated (e.g., > 0.95), they introduce multicollinearity, which can destabilize coefficient estimates in linear models and reduce interpretability. Removing one feature from each correlated pair based on domain knowledge or its correlation with the target variable preserves predictive power while reducing redundancy. This approach is more targeted than PCA, which transforms features into uncorrelated components but sacrifices interpretability and may not align with the binary target.

Answer

Apply PCA to all features to decorrelate them.

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Standardize all features using StandardScaler.

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Randomly drop half of the correlated features.

Question 11

A data scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?

Accepted Answer

Perform the Augmented Dickey-Fuller (ADF) test.. The Augmented Dickey-Fuller (ADF) test is a formal statistical hypothesis test specifically designed to check for stationarity in a time series. It tests the null hypothesis that a unit root is present, indicating non-stationarity, against the alternative of stationarity. This makes it the most appropriate EDA technique for directly assessing stationarity.

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Plot the autocorrelation function (ACF).

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Use time-series cross-validation.

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Create a scatter plot of the series against its lag.

Question 12

During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?

Accepted Answer

Apply log transformation.. A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative data.

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Standardize the features using Z-scores.

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Apply a sine transformation.

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Apply Box-Cox transformation with lambda=0.

Question 13

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?

Accepted Answer

Use Z-score and flag values with absolute Z-score > 3.. The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.

Answer

Apply a Random Forest classifier to predict outliers.

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Remove any value that is more than one standard deviation from the mean.

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Use DBSCAN clustering with default parameters.

Question 14

Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?

Accepted Answer

Check the proportion of missing values for each feature.. Option A is correct because checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.

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Encode all categorical features using label encoding for simplicity.

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Include all categorical features with high cardinality as-is in the model.

Question 15

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has 1 million rows with 50 features, including numerical and categorical variables. The goal is to identify patterns and potential data quality issues before building a model. Which approach should the data scientist take to efficiently explore the data?

Accepted Answer

Use AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions.. AWS Glue DataBrew is purpose-built for visual data preparation and profiling without writing code. It can directly profile the 1-million-row dataset, automatically generate data quality reports (e.g., missing values, outliers, data types), and provide distribution visualizations for both numerical and categorical features, making it the most efficient choice for exploratory data analysis.

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Use Amazon Athena to run SQL queries and generate summary statistics.

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Use Amazon SageMaker Data Wrangler to import the data and create a flow for feature engineering.

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Use Amazon SageMaker Ground Truth to label the data and then analyze the labels.