MLS-C01 Exploratory Data Analysis Practice Test 13 — 15 Questions

Question 1

A data engineer is building a data pipeline that aggregates customer transaction data. The engineer notices that some transactions have duplicate entries due to a system error. Which approach should the engineer use to identify and remove duplicates based on a unique transaction ID?

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

Accepted Answer

Use the drop_duplicates method on the transaction ID column. Using drop_duplicates on the transaction ID column is a straightforward and efficient method to remove duplicate rows based on the unique identifier. Option A is incorrect; sorting and checking consecutive rows is a valid but more complex approach, and not as direct as drop_duplicates. Option B is incorrect because fuzzy matching is designed for approximate matches, not exact duplicates. Option C is incorrect because grouping by all columns and summing would aggregate data, potentially losing information, and does not specifically remove duplicate transaction IDs.

Answer

Sort the data by transaction ID and then check consecutive rows for equality

Answer

Use fuzzy matching to find similar transaction IDs

Answer

Group by all columns and aggregate with sum

Question 2

A machine learning team is analyzing feature importance in a dataset with many categorical features. They plan to use a tree-based model. Which encoding method should they use to handle high-cardinality categorical features without creating too many dummy variables?

Accepted Answer

Target encoding. Target encoding replaces categories with the mean of the target, which is efficient and works well with tree models. Option A is wrong because one-hot encoding creates many columns for high cardinality. Option B is wrong because label encoding imposes ordinality. Option D is wrong because frequency encoding may not capture predictive information.

Answer

One-hot encoding

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Label encoding

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Frequency encoding

Question 3

A data analyst wants to understand the distribution of a continuous variable. Which visualization is most appropriate for this purpose?

Accepted Answer

Histogram. (Histogram) is correct because a histogram displays the frequency distribution of a continuous variable by grouping data into bins. Option A (Box plot) is incorrect because it shows summary statistics (median, quartiles, outliers) but not the full distribution. Option B (Bar chart) is incorrect because bar charts are for categorical data, not continuous. Option D (Scatter plot) is incorrect because it shows the relationship between two continuous variables, not the distribution of a single variable.

Answer

Box plot

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Bar chart

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Scatter plot

Question 4

A data scientist is analyzing a dataset and finds that two features have a Pearson correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose two.)

Accepted Answer

Combine the two features into a single feature using PCA or averaging. A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity can inflate coefficient variances and reduce model interpretability. Two standard remedies are to remove one of the correlated features (Option D) or to combine them into a single feature using techniques like PCA, averaging, or summing (Option A). Option B (adding interaction terms) would introduce additional correlated terms and exacerbate multicollinearity. Option C (increasing regularization) can help stabilize coefficients but does not directly address the high pairwise correlation; it is often used as a secondary technique after feature selection or combination. Option E (standard scaling) does not change the correlation coefficient and therefore does not mitigate multicollinearity.

Answer

Add interaction terms between the features

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Increase regularization strength in the model

Answer

Apply standard scaling to both features

Question 5

A data engineer is performing exploratory data analysis on a dataset with 1 million rows and 50 features. The engineer wants to identify missing values and outliers. Which THREE approaches should the engineer use? (Choose three.)

Accepted Answer

Use a DataFrame.info() method to see non-null counts. Options B, D, and E are correct because they directly help identify missing values and outliers. DataFrame.info() shows non-null counts per column, revealing missing values. The missingno matrix visualizes missing data patterns across rows and columns. DataFrame.describe() provides summary statistics (count, mean, std, min, max, quartiles) that can indicate outliers (e.g., values far outside the interquartile range). Option A (correlation heatmap) is used to assess relationships between features, not to detect missing values or outliers. Option C (box plots for all features simultaneously) is impractical with 50 features because it would be cluttered and hard to interpret; box plots are more useful when applied selectively to a few features or after filtering.

Answer

Create a correlation heatmap of all features

Answer

Plot box plots for all features simultaneously

Question 6

A data scientist is exploring a dataset with a binary target variable. Which TWO metrics are appropriate for evaluating the balance of the target classes? (Choose two.)

Accepted Answer

Count plot of the target variable. Options A and D are correct. A count plot directly visualizes the frequency of each class in the target variable, making it easy to assess balance. Similarly, value_counts() returns the exact count of each class, providing a numeric measure of balance. Option B (histogram) is designed for continuous variables; while it can depict the counts of two categories, it is not the standard or most appropriate method for evaluating binary class balance. Option C (scatter plot) is used to explore relationships between two numeric features and does not show class distribution. Option E (correlation matrix) measures linear relationships between numeric features and is irrelevant for assessing target class balance.

Answer

Histogram of a feature

Answer

Scatter plot of two features colored by target

Answer

Correlation matrix of all features

Question 7

A data science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. Which course of action is most appropriate?

Accepted Answer

Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns. Binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because concluding non-predictiveness based solely on correlation ignores potential non-linear patterns. Option B is premature since feature importance from a random forest model is typically used after feature engineering, not during initial EDA. Option D is a data transformation that addresses skewness but does not directly help understand the relationship with the target; it would be more appropriate as a preprocessing step.

Answer

Conclude that 'transaction amount' is not predictive because the correlation is near zero

Answer

Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'

Answer

Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis

Question 8

A data scientist is performing exploratory data analysis on a dataset with missing values. They want to understand the distribution of each feature and identify outliers. Which AWS service can be used to create visualizations such as histograms and box plots without writing any code?

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a serverless, machine learning-powered business intelligence service that allows users to create interactive dashboards and visualizations without writing code. Option A is wrong because Amazon EMR is a big data platform, not primarily for visualization. Option B is wrong because AWS Glue is used for ETL, not visualization. Option C is correct. Option D is wrong because Amazon SageMaker Studio requires coding for custom visualizations. Option E is wrong because Amazon Athena is a query service, not a visualization tool.

Answer

Amazon EMR

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AWS Glue

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Amazon SageMaker Studio

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Amazon Athena

Question 9

During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A (PCA) is wrong because dimensionality reduction does not address class imbalance. Option B (removing outliers) is wrong because it may worsen imbalance and is not a standard technique for imbalance. Option C (cross-validation) is a model evaluation technique, not a solution for imbalance. Option D (feature scaling) does not affect class distribution.

Answer

Apply PCA to reduce dimensionality

Answer

Remove outliers from the majority class

Answer

Use cross-validation to evaluate the model

Answer

Apply feature scaling to all features

Question 10

A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is the most appropriate service for this task because it provides a visual, no-code interface for data preparation and profiling. It can automatically compute summary statistics (mean, median, standard deviation) for numerical features and count distinct values for categorical features without writing any code. Amazon Athena requires writing SQL queries, which is not 'minimal coding' and is less suitable for profiling. AWS Glue ETL jobs require writing Python or Scala code, so it is more code-intensive. Amazon SageMaker Data Wrangler also requires some setup and integration with SageMaker, and while it can perform similar tasks, it is not as straightforward for simple profiling as DataBrew. Amazon EMR requires managing clusters and writing code, making it the least minimal coding option.

Answer

Amazon Athena

Answer

AWS Glue ETL jobs

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Amazon SageMaker Data Wrangler

Answer

Amazon EMR

Question 11

A data scientist is performing EDA on a time-series dataset and observes a strong upward trend and seasonal patterns. The scientist needs to make the data stationary for modeling. Which transformation should be applied?

Accepted Answer

Apply differencing to the series. Differencing is a technique that removes trends and seasonality by subtracting the previous observation from the current one, making the time series stationary. One-hot encoding (A) is used for categorical variables. PCA (B) reduces dimensionality but does not address stationarity. Min-max scaling (C) normalizes the range of data but does not remove trend or seasonality. Logarithmic transformation (E) stabilizes variance but does not eliminate trends. Therefore, differencing (D) is the correct choice.

Answer

Apply one-hot encoding

Answer

Apply PCA

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Apply min-max scaling

Answer

Apply logarithmic transformation

Question 12

A data scientist is analyzing a dataset with high cardinality categorical features (e.g., user IDs with millions of unique values). They want to visualize the relationship between these categorical features and a continuous target variable. Which approach is most effective for EDA?

Accepted Answer

Group rare categories into an 'Other' category and use box plots. For high cardinality categorical features like user IDs with millions of unique values, directly visualizing all categories is infeasible. Grouping rare categories into an 'Other' category reduces cardinality, enabling effective comparison of the continuous target distribution across categories using box plots. This approach preserves meaningful information while avoiding clutter. Option B (one-hot encoding) creates an excessively wide feature set unsuitable for simple visualization. Option C (bar chart with all categories) would be overcrowded and unreadable. Option D (removing features) discards potentially valuable information. Option E (feature hashing) is more appropriate for modeling pipelines, not for intuitive EDA visualization. Thus, Option A is the most effective approach.

Answer

Apply one-hot encoding and use scatter plots

Answer

Use a bar chart with all categories on x-axis

Answer

Remove the categorical features from analysis

Answer

Apply feature hashing and visualize the hashed values

Question 13

During EDA, a data scientist discovers that two numerical features have a Pearson correlation coefficient of 0.95. Which action should the scientist take to avoid multicollinearity in a linear regression model?

Accepted Answer

Remove one of the features. Pearson correlation of 0.95 indicates high multicollinearity, which can adversely affect linear regression by inflating standard errors. Removing one of the correlated features (Option A) is a straightforward solution to eliminate multicollinearity. Option B (PCA) reduces dimensionality but creates principal components that are linear combinations, losing interpretability; it also does not directly remove the original features. Option C (Ridge regression) applies L2 regularization to shrink coefficients, which can mitigate multicollinearity but does not remove it; simply removing one feature is simpler. Option D (polynomial features) would introduce more correlated terms, worsening multicollinearity. Option E (min-max scaling) does not affect correlation. Therefore, removing one feature is the best action.

Answer

Apply PCA to the two features

Answer

Use Ridge regression to penalize coefficients

Answer

Create polynomial features from the correlated pair

Answer

Apply min-max scaling to both features

Question 14

A data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?

Accepted Answer

Use pandas .isnull().sum() in a SageMaker notebook. Using pandas .isnull().sum() in a SageMaker notebook is the most appropriate method because it directly provides the count (and thus the percentage when divided by total rows) of missing values per column, which is a standard exploratory data analysis technique. Option A is incorrect because Amazon S3 Select is used for filtering and retrieving subsets of data from S3 objects, not for computing missing values. Option B is incorrect because while Amazon Athena can run SQL queries like SELECT COUNT(*), it is less direct for per-column missing value analysis and requires a schema. Option C is incorrect because Amazon QuickSight is a visualization tool, not designed for programmatic missing value detection. Option D is incorrect because AWS Glue Crawler discovers schema and partitions, not missing values.

Answer

Use Amazon S3 Select to query missing values

Answer

Use Amazon Athena to run a SELECT COUNT(*) query

Answer

Use Amazon QuickSight to create a missing value dashboard

Answer

Use AWS Glue Crawler to detect missing values

Question 15

A data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?

Accepted Answer

Create a correlation matrix and visualize with a heatmap. Create a correlation matrix and visualize with a heatmap. This technique directly shows pairwise correlations between features, making it easy to identify groups of highly correlated (redundant) features. Option A is incorrect: t-SNE is a dimensionality reduction technique for visualization of high-dimensional data, but it does not quantify pairwise correlations between features. Option B is incorrect: PCA reduces dimensionality by creating principal components that are linear combinations of original features; while loadings indicate feature contributions, they do not directly show pairwise correlations between original features. Option C is incorrect: Mutual information measures dependency between features and target, not between features themselves. Option D is incorrect: The chi-square test is used for testing association between categorical variables, not for continuous features or pairwise correlation analysis.

Answer

Use t-SNE to visualize feature relationships

Answer

Apply PCA and examine the loadings

Answer

Compute mutual information between each feature and the target

Answer

Use chi-square test for each pair