MLS-C01 Exploratory Data Analysis Practice Test 12 — 15 Questions

Question 1

A data scientist is working with a dataset containing 10,000 observations and 100 features. The scientist wants to detect outliers in the dataset. Which method is most appropriate for outlier detection in a high-dimensional space?

Accepted Answer

Use Isolation Forest. Option D is correct because Isolation Forest is effective for high-dimensional data and uses tree-based isolation. Option A is wrong because Z-score assumes normality and is univariate. Option B is wrong because IQR is univariate and not suitable for high dimensions. Option C is wrong because Mahalanobis distance assumes multivariate normality and is sensitive to dimensionality.

Answer

Use Z-score to identify points beyond 3 standard deviations

Answer

Use Mahalanobis distance

Answer

Use interquartile range (IQR) for each feature

Question 2

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?

Accepted Answer

Use the drop_duplicates method on the transaction ID column. Option B is correct because dropping duplicates based on the transaction ID is straightforward and efficient. Option A is wrong because groupby with aggregation may lose information. Option C is wrong because fuzzy matching is for approximate matches, not exact duplicates. Option D is wrong because sorting then checking consecutive equals is more complex than needed.

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 3

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. Option C is correct because 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

Answer

Label encoding

Answer

Frequency encoding

Question 4

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

Accepted Answer

Histogram. Option B is correct because a histogram shows the frequency distribution of a continuous variable. Option A is wrong because a scatter plot shows relationship between two variables. Option C is wrong because a box plot shows summary statistics but not full distribution. Option D is wrong because a bar chart is for categorical data.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 5

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. Options B and C are correct. High correlation can lead to multicollinearity, so removing one feature (B) or combining them (C) are valid approaches. Option A is wrong because increasing regularization is a remedy for multicollinearity but does not directly address the correlation. Option D is wrong because scaling does not affect correlation. Option E is wrong because adding interaction terms can increase multicollinearity.

Answer

Add interaction terms between the features

Answer

Increase regularization strength in the model

Answer

Apply standard scaling to both features

Question 6

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 A, C, and D are correct. A gives overall count, C provides visual summary, D gives statistical summary. Option B is wrong because box plots are for continuous variables, not for all features. Option E is wrong because correlation heatmap does not show missing values or outliers.

Answer

Create a correlation heatmap of all features

Answer

Plot box plots for all features simultaneously

Question 7

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. Count plot and value_counts show class frequencies. Option B is wrong because correlation matrix shows relationships between features. Option C is wrong because scatter plot shows relationship between two numeric variables. Option E is wrong because histogram shows distribution of a continuous variable.

Answer

Histogram of a feature

Answer

Scatter plot of two features colored by target

Answer

Correlation matrix of all features

Question 8

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. Option B is correct because binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because log transformation does not reveal relationship with target. Option C is wrong because correlation is already computed and can mask non-linearity. Option D is wrong because feature importance from a tree model is more appropriate after feature engineering, not during EDA.

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 9

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 SageMaker Studio requires coding for custom visualizations. Option B is wrong because AWS Glue is used for ETL, not visualization. Option D is wrong because Amazon Athena is a query service. Option E is wrong because Amazon EMR is a big data platform not primarily for visualization.

Answer

Amazon EMR

Answer

AWS Glue

Answer

Amazon SageMaker Studio

Answer

Amazon Athena

Question 10

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 is wrong because removing outliers does not address class imbalance. Option B is wrong because feature scaling does not affect imbalance. Option D is wrong because PCA is for dimensionality reduction, not imbalance. Option E is wrong because cross-validation is a model evaluation technique, not an imbalance solution.

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 11

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 provides a visual interface for data preparation and profiling, including summary statistics and distinct value counts, without writing code. Option A is wrong because Amazon SageMaker Data Wrangler requires integration with SageMaker and may require more setup. Option B is wrong because AWS Glue ETL jobs require coding in Python or Scala. Option D is wrong because Amazon Athena requires SQL queries. Option E is wrong because Amazon EMR requires cluster management and coding.

Answer

Amazon Athena

Answer

AWS Glue ETL jobs

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon EMR

Question 12

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 common technique to remove trends and seasonality to make a time series stationary. Option B is wrong because logarithmic transformation stabilizes variance but does not remove trends. Option C is wrong because min-max scaling does not address trends. Option D is wrong because one-hot encoding is for categorical variables. Option E is wrong because PCA is for dimensionality reduction.

Answer

Apply one-hot encoding

Answer

Apply PCA

Answer

Apply min-max scaling

Answer

Apply logarithmic transformation

Question 13

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, grouping rare categories into an 'Other' category reduces cardinality and allows meaningful visualizations like box plots. Option A is wrong because removing the feature loses information. Option B is wrong because one-hot encoding creates too many columns and is not suitable for visualization. Option D is wrong because visualizing millions of categories is not feasible. Option E is wrong because feature hashing is for modeling, not EDA visualization.

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 14

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. High correlation indicates multicollinearity, which can be addressed by removing one of the correlated features. Option A is wrong because PCA reduces dimensionality but loses interpretability. Option B is wrong because regularization (e.g., Ridge) can handle multicollinearity but does not remove it; removing one feature is simpler. Option D is wrong because polynomial features introduce more multicollinearity. Option E is wrong because scaling does not address correlation.

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 15

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 a standard approach to count missing values per column. Option A is wrong because S3 Select is for filtering S3 objects, not for data analysis. Option B is wrong because QuickSight is for visualization but not for programmatic missing value analysis. Option D is wrong because Athena requires SQL and is less direct for EDA. Option E is wrong because Glue Crawler discovers schema, 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