MLS-C01 Exploratory Data Analysis — Timed 10 Questions

Question 1

A machine learning engineer is working on a customer churn prediction project. The dataset contains 100,000 records with 15 features, including customer demographics, account information, and usage patterns. The target variable 'churned' is binary with 15% positive examples. During EDA, the engineer notices that the feature 'tenure' (number of months the customer has been with the company) has a multimodal distribution with peaks at 1, 12, 24, and 36 months. Also, the feature 'monthly_charges' has a strong positive correlation with 'total_charges' (correlation coefficient = 0.95). The engineer wants to build a logistic regression model. Which preprocessing steps should the engineer take to address these issues? (Select TWO.)

10 minute time limit — choose an answer to begin.

Accepted Answer

Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship.. Binning the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) captures the multimodal distribution and non-linear relationship with churn, which logistic regression (a linear model) cannot model directly. This transforms the feature into a format that allows the model to learn different churn probabilities for each tenure segment without imposing a linear assumption. Option B is correct because 'monthly_charges' and 'total_charges' have a very high positive correlation (0.95), leading to multicollinearity. Removing one of these features (e.g., 'total_charges') reduces multicollinearity, which improves the stability and interpretability of logistic regression coefficients.

Answer

Apply log transformation to the 'tenure' feature to make it unimodal.

Answer

Create polynomial features up to degree 3 for 'tenure' to capture non-linearity.

Answer

Standardize all numerical features to have mean 0 and variance 1.

Question 2

A company has a dataset with a large number of missing values in several columns. The data scientist wants to impute missing values without introducing bias. Which approach should be used?

Accepted Answer

Use iterative imputation (MICE) to model missing values. Multiple Imputation by Chained Equations (MICE) models each variable with missing values as a function of other variables, reducing bias compared to simpler methods. Option A is wrong because removing rows with missing values leads to data loss and potential bias if missingness is not random. Option C is wrong because replacing with the mode ignores relationships between variables and can introduce bias. Option D is wrong because mean imputation reduces variance and can distort relationships.

Answer

Remove rows with any missing values

Answer

Replace missing values with the mode of each column

Answer

Replace missing values with the mean of each column

Question 3

Which TWO of the following are valid reasons to use a sample of the data during exploratory data analysis instead of the full dataset? (Select TWO.)

Accepted Answer

Reduce memory usage and computation time. Sampling is used during exploratory data analysis to reduce memory usage and computation time (Option D) and to enable interactive data visualization with large datasets (Option E). Option A is incorrect because sampling does not remove bias; it can introduce bias if not done properly. Option B is incorrect because sampling often misses rare events rather than ensuring they are captured. Option C is incorrect because reducing the amount of data typically reduces model accuracy, not improves it.

Answer

Remove bias from the original dataset

Answer

Ensure rare events are captured in the analysis

Answer

Improve model accuracy by reducing noise

Question 4

A company has customer feedback data stored in CSV files in S3. The data includes a 'feedback_text' column. Which AWS service is best suited for performing sentiment analysis as part of exploratory data analysis?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can perform sentiment analysis directly on text data. Option B is wrong because Amazon Rekognition is for image and video analysis, not text sentiment. Option C is wrong because Amazon Textract is for extracting text from documents, not analyzing sentiment. Option D is wrong because Amazon Lex is for building conversational interfaces (chatbots), not for sentiment analysis.

Answer

Amazon Rekognition

Answer

Amazon Textract

Answer

Amazon Lex

Question 5

During EDA, a data scientist finds that a feature has a skewed distribution. They want to apply a log transformation to make it more Gaussian-like. Which Amazon SageMaker feature is most appropriate for this transformation?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler is the most appropriate feature for applying a log transformation to make a skewed distribution more Gaussian-like. It provides a visual interface that allows data scientists to easily apply transformations like log scaling without writing code. Other options are not suitable: SageMaker Ground Truth is for data labeling, SageMaker JumpStart provides pre-built models and solutions, and SageMaker Clarify is used for bias detection and model explainability.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker JumpStart

Answer

Amazon SageMaker Ground Truth

Question 6

A company has a dataset with 1 million rows and 500 features. They want to reduce dimensionality for visualization. Which technique is most suitable for preserving global structure?

Accepted Answer

Principal Component Analysis (PCA). (PCA) is correct because PCA is a linear dimensionality reduction technique that preserves global variance, making it suitable for visualizing the overall structure of high-dimensional data. Option A (Autoencoder) is a neural network approach that can preserve both local and global structure but is more complex and not primarily designed for visualization. Option B (t-SNE) focuses on preserving local structure, not global. Option C (LDA) requires labeled data and is used for classification, not general dimensionality reduction.

Answer

Autoencoder

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Question 7

A company is performing EDA on a dataset with 10,000 rows and 200 columns. They run a correlation matrix and find many high correlations (|r| > 0.9). What is the best approach to address multicollinearity before modeling?

Accepted Answer

Calculate Variance Inflation Factor (VIF) and remove features with VIF > 10. Variance Inflation Factor (VIF) quantifies how much a feature is correlated with others; removing features with VIF > 10 reduces multicollinearity. Option A is incorrect because standardizing (scaling) does not remove correlations between features. Option C is incorrect because Lasso regression can handle multicollinearity through regularization, but it is a modeling technique and not the best first step during EDA for identifying and removing redundant features. Option D is incorrect because PCA creates orthogonal (uncorrelated) components, which eliminates multicollinearity, but it reduces interpretability and is a dimensionality reduction technique applied during modeling, not an EDA step for directly addressing multicollinearity in the original features.

Answer

Standardize all features

Answer

Use Lasso regression with cross-validation

Answer

Apply Principal Component Analysis (PCA) to all features

Question 8

A data scientist is analyzing a dataset where the target variable is highly imbalanced (1% positive class). They are performing EDA. Which metric is most appropriate for evaluating class separation in the feature space?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). AUC-ROC is the most appropriate metric for evaluating class separation in the feature space during EDA, especially with imbalanced data, because it measures the model's ability to distinguish between classes across all classification thresholds and is invariant to the proportion of positive and negative samples. Accuracy is misleading on highly imbalanced data (e.g., 99% accuracy would be trivial). F1 score is a model evaluation metric that combines precision and recall, but it is not directly suitable for assessing feature separability in EDA; it is typically used after model training. Log loss measures the probabilistic confidence of predictions but does not directly indicate class separation in the feature space.

Answer

Accuracy

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F1 score

Answer

Log loss

Question 9

A data scientist is exploring a dataset containing customer transactions. The dataset has a column 'transaction_amount' with values ranging from $0.01 to $10,000. Which EDA step is most appropriate to detect skewed distribution?

Accepted Answer

Create a histogram of transaction amounts. A histogram or density plot reveals skewness visually. Option A is wrong because count plot is for categorical data. Option B is wrong because scatter plot shows relationship between two variables. Option D is wrong because heatmap shows correlation, not skewness.

Answer

Create a count plot of transaction amounts

Answer

Create a correlation heatmap of all numeric columns

Answer

Create a scatter plot of transaction amount vs. customer age

Question 10

During EDA, a data scientist finds that a feature 'age' has 30% missing values. The dataset has 100,000 rows. Which imputation strategy is most robust if the data is not missing at random (MNAR) and the missingness is related to the age value itself?

Accepted Answer

Create a separate category indicating missingness and impute with a placeholder. When data are missing not at random (MNAR) and missingness is related to the age value itself, creating a separate category for missingness allows the model to capture the systematic difference. Imputing with a placeholder (e.g., -1) and adding an indicator column preserves the missingness pattern. Option A is wrong because mean imputation ignores the systematic difference and reduces variance. Option B is wrong because median imputation similarly fails to account for the non-random missingness. Option C is wrong because random sampling from observed ages does not capture the relationship between missingness and the value.

Answer

Impute with the mean age

Answer

Impute with a random sample from the observed ages

Answer

Impute with the median age