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

Question 1

A data scientist is performing EDA on a dataset with a timestamp column. They want to detect seasonality. Which visualization is most appropriate?

Accepted Answer

Line plot of value over time. A line plot of value over time is the most appropriate visualization to detect seasonality because it displays the continuous temporal trend and repeating patterns (e.g., daily, weekly, yearly cycles) directly. By plotting the timestamp on the x-axis and the value on the y-axis, seasonal peaks and troughs become visually apparent. Other plots aggregate or lose the temporal ordering needed to see cyclicality.

Answer

Box plot of value grouped by month

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Bar chart of average value per month

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Scatter plot of timestamp vs. value

Question 2

A machine learning team is analyzing a dataset with 10,000 rows and 200 features. They suspect data leakage due to time-based features. Which THREE EDA checks should they perform?

Accepted Answer

Plot distribution of each feature in training vs. test sets. Option A is correct because plotting each feature's distribution in the training versus test sets reveals covariate shift or leakage-driven discrepancies, such as a feature whose test distribution is unrealistically similar to the training target or whose range differs sharply, which is a classic symptom of time-based leakage when random splits are used. Option C is correct because time-based leakage often stems from the data being sorted chronologically or from features that encode future information (e.g., rolling aggregates computed over later timestamps); checking sort order and whether any feature references future events directly detects this. Option D is correct because comparing each feature's correlation with the target in the training versus test sets exposes features that are suspiciously predictive in one split but not the other, a strong indicator that the feature leaks target or future information across the split boundary. Option B is not appropriate because PCA component separation of train/test is a dimensionality-reduction visualization, not a leakage diagnostic, and it would not pinpoint time-based leakage. Option E is not appropriate because k-means clustering on the whole dataset ignores the train/test split and cannot reveal leakage; it only describes cluster structure.

Answer

Apply PCA and check if first two components separate train/test

Answer

Perform k-means clustering on the whole dataset

Question 3

During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?

Accepted Answer

Compute summary statistics (min, max, percentiles). Option B is correct because computing summary statistics such as min, max, and percentiles (e.g., with pandas describe() or quantile()) quantifies the distribution of 'age' and reveals how many values fall outside the expected 18-100 range, which is the proper first step in EDA before deciding on any treatment. Option E is correct because a box plot visualizes the median, interquartile range, and points beyond the whiskers (typically 1.5*IQR), directly exposing outliers such as ages near 0 or 150 and showing whether they are isolated or systematic. Option A is not appropriate because deleting rows with age > 100 is a premature data-destruction step; the scientist should first investigate whether those values are data-entry errors, unit issues, or legitimate cases before removing anything. Option C is not appropriate because a log transformation is a modeling/preprocessing technique for skewed distributions and does nothing to diagnose why ages of 0 or 150 exist. Option D is not appropriate because imputing out-of-range ages with the mean would mask the anomaly and distort the distribution before the cause of the invalid values has been investigated.

Answer

Remove all rows with age > 100

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Apply log transformation to normalize the distribution

Answer

Impute age values outside 18-100 with the mean

Question 4

A data scientist is analyzing a dataset with 500 features and 10,000 rows. The target variable is binary. After training a logistic regression model, the coefficients show many non-zero values but the model has low accuracy on the test set. Which EDA step should the data scientist perform next to improve model performance?

Accepted Answer

Use correlation analysis or mutual information to select the most relevant features.. With 500 features and low accuracy, the model likely suffers from overfitting due to irrelevant or redundant features. Correlation analysis or mutual information helps select the most relevant features, reducing noise and improving generalization. Option A (PCA) reduces dimensionality but creates uninterpretable components and may lose feature relationships, not directly addressing irrelevant features. Option B (collect more data) may help but does not solve the core issue of irrelevant features. Option C (normalization) only scales features, not reduce them, and logistic regression is not sensitive to scale if coefficients are interpreted carefully; overfitting is more likely due to too many features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.

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Collect more training data to improve generalization.

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Normalize the features using StandardScaler.

Question 5

An ML engineer is performing EDA on a dataset of customer transactions. The dataset has 1 million rows and 20 columns, including a 'transaction_amount' column. The engineer notices that 5% of the transaction amounts are negative, which are data entry errors. The rest are positive. Which approach is most appropriate for handling these negative values during EDA?

Accepted Answer

Remove rows with negative transaction amounts from the dataset.. Negative transaction amounts are explicitly identified as data entry errors, not legitimate refunds or reversals. During EDA, the cleanest and most defensible treatment is to remove those erroneous rows so they do not distort summary statistics, distributions, or downstream feature engineering. Imputing or transforming them would fabricate data that never existed.

Answer

Impute the negative values with the median of positive transaction amounts.

Answer

Take the absolute value of the negative transaction amounts.

Answer

Cap the negative values at zero.

Question 6

A data scientist is exploring a dataset and wants to understand the distribution of a continuous feature. Which visualization is most appropriate for identifying skewness and potential outliers?

Accepted Answer

Box plot. A box plot summarizes a continuous distribution using the five-number summary (min, Q1, median, Q3, max) and explicitly draws whiskers at 1.5×IQR, so points beyond the whiskers are flagged as potential outliers. The position of the median within the box and the symmetry of the whiskers make skewness visually obvious. That combination is exactly what the question asks for.

Answer

Bar chart

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Scatter plot

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Heatmap

Question 7

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

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Impute with a random sample from the observed ages

Answer

Impute with the median age

Question 8

A data scientist is performing EDA on a dataset with both numerical and categorical features. Which technique is best for detecting multicollinearity among numerical features?

Accepted Answer

Correlation matrix with heatmap. A correlation matrix with heatmap computes pairwise Pearson (or Spearman) correlation coefficients between all numerical features, making it the standard EDA technique for spotting multicollinearity. High absolute values (typically |r| > 0.8 or 0.9) between two predictors signal redundancy that can destabilize linear models. The heatmap visualizes these coefficients so the analyst can quickly identify which features to drop or combine.

Answer

Chi-square test of independence

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Box plots for each numerical feature

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Pair plot

Question 9

A data scientist is working with a dataset containing text reviews. The goal is to classify sentiment. During EDA, they compute the word frequency distribution. They notice that the most frequent words are common stop words like 'the', 'and', 'a'. Which action should they take to improve the feature representation for modeling?

Accepted Answer

Remove the stop words from the text before creating the bag-of-words representation.. Stop words like 'the', 'and', and 'a' carry little semantic meaning for sentiment classification and dominate the bag-of-words feature space, adding noise and dimensionality without discriminative value. Removing them before vectorization reduces feature count, speeds training, and lets the model focus on sentiment-bearing terms like 'excellent', 'terrible', or 'disappointing'. This is a standard text preprocessing step in NLP pipelines for sentiment analysis.

Answer

Use n-grams instead of unigrams to capture phrase patterns.

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Add more stop words to the default list to remove even more common words.

Answer

Apply stemming to reduce words to their root forms.

Question 10

After loading a dataset into a pandas DataFrame, a data scientist runs df.info() and sees that a column 'income' has object dtype. What does this indicate, and what EDA step should be taken?

Accepted Answer

The column contains strings; convert to numeric using pd.to_numeric() and investigate non-convertible values.. In pandas, an 'object' dtype column typically means the column contains Python string objects (or mixed types), not numeric values. For an 'income' column, this usually indicates the data was read as strings — possibly due to currency symbols, commas, or non-numeric entries. The correct EDA step is to convert with pd.to_numeric() (using errors='coerce' to identify non-convertible values) and then investigate those values (e.g., 'N/A', '$50,000', blanks) before deciding on imputation or cleaning.

Answer

The column has missing values; impute them.

Answer

Normalize the column to a 0-1 range.

Answer

The column is already numeric; proceed.

Question 11

A data scientist is performing EDA on a dataset with 100 features. They want to reduce dimensionality by removing highly correlated features. Which TWO approaches are appropriate? (Choose TWO.)

Accepted Answer

Compute a correlation matrix and remove one feature from each pair with correlation >0.95.. Option C is correct because computing a correlation matrix (e.g., pandas .corr() or numpy.corrcoef) and dropping one feature from each pair whose absolute correlation exceeds 0.95 directly targets the stated goal of removing highly correlated features, reducing redundancy while preserving one representative per correlated group. Option D is correct because PCA transforms the 100 features into orthogonal principal components, and selecting the components that cumulatively explain 95% of the variance achieves dimensionality reduction while eliminating the multicollinearity that correlated features introduce. Option A is not appropriate because random forest feature importance ranks features by predictive contribution, not by correlation, so it does not specifically remove highly correlated features. Option B is not appropriate because VarianceThreshold removes low-variance (near-constant) features, which is unrelated to correlation-based redundancy. Option E is not appropriate because L1 regularization is applied during model training to shrink coefficients, not as an EDA preprocessing step for explicitly detecting and removing correlated features.

Answer

Use feature importance from a random forest to select top features.

Answer

Remove features with low variance using VarianceThreshold.

Answer

Apply L1 regularization (Lasso) during model training to zero out coefficients of correlated features.

Question 12

During EDA of a dataset for a regression problem, a data scientist notices that the target variable has a right-skewed distribution. Which THREE transformations are appropriate to address this skewness? (Choose THREE.)

Accepted Answer

Log transformation. Options A, C, and D are correct. Log transformation, Box-Cox transformation, and Yeo-Johnson transformation are effective methods for reducing right skewness in the target variable. Option B (StandardScaler) standardizes features to have zero mean and unit variance, but does not reduce skewness. Option E (Min-Max scaling) scales features to a fixed range, but does not affect the shape of the distribution.

Answer

StandardScaler (z-score normalization)

Answer

Min-Max scaling

Question 13

A data scientist is exploring a dataset with categorical variables. Which TWO EDA techniques are appropriate for understanding the relationship between a categorical feature and a continuous target? (Choose TWO.)

Accepted Answer

Violin plots. Violin plots (B) are correct because they combine a box plot with a kernel density estimate, showing the full distribution of the continuous target within each category, which reveals differences in median, spread, skewness, and multimodality across categories. Side-by-side box plots (E) are also correct because they display the median, quartiles, and outliers of the continuous target for each categorical group, making group-to-group comparisons of central tendency and dispersion straightforward. A correlation matrix (A) is inappropriate because Pearson/Spearman correlation requires numeric variables and cannot directly encode a nominal categorical feature. A scatter plot with the categorical variable on the x-axis (C) is not ideal since the categorical axis has no meaningful numeric spacing, so it degenerates into vertical strips and is better replaced by jittered or violin/box representations. A bar chart of category counts (D) only shows the frequency of each category and says nothing about the continuous target's distribution or relationship with the categorical feature.

Answer

Correlation matrix

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Scatter plot with categorical variable on x-axis

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Bar chart of category counts

Question 14

A data scientist is trying to upload a CSV file to an S3 bucket using the AWS CLI without specifying server-side encryption. The upload fails with an AccessDenied error. Based on the bucket policy exhibit, what is the most likely cause?

Accepted Answer

The upload request did not specify the required server-side encryption.. When an S3 bucket policy includes a condition requiring server-side encryption (e.g., s3:PutObject with a StringNotEquals on s3:x-amz-server-side-encryption), any upload that omits the encryption header is denied. The AWS CLI's default cp command does not automatically add SSE headers, so the request fails with AccessDenied even though the IAM identity has baseline permissions. The fix is to add --sse AES256 or --sse aws:kms to the CLI command.

Answer

The bucket does not exist.

Answer

The data scientist does not have any permissions to the bucket.

Answer

The data scientist used the wrong AWS region.

Question 15

A data scientist is analyzing a dataset with missing values. Which technique is most appropriate for imputing missing values in a numerical feature that follows a normal distribution?

Accepted Answer

Mean imputation. Mean imputation is suitable for normally distributed data as it preserves the mean. Median is robust to outliers, not normality. Mode is for categorical data. Standard deviation is not an imputation method. KNN imputation is non-parametric.

Answer

Standard deviation imputation

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Mode imputation

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Median imputation