MLS-C01 Exploratory Data Analysis Practice Test 24 — 15 Questions

Question 1

A data scientist is building a regression model to predict house prices. The dataset includes a feature 'zip_code' with 1,000 unique values. What is the best way to handle this categorical feature in the exploratory data analysis phase?

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

Accepted Answer

Apply target encoding using the mean house price per zip code. Target encoding (option B) is the best approach for high-cardinality categorical features like zip_code. It captures the relationship between the category and the target variable (house price) without creating an excessive number of dummy variables. One-hot encoding (option A) would create 1,000 columns, leading to high dimensionality and sparsity. Label encoding (option D) implies an ordinal relationship, which does not exist for zip codes. Frequency encoding (option C) may not capture price variation well because two zip codes with the same frequency could have very different average prices.

Answer

One-hot encode the zip_code feature

Answer

Replace zip_code with the frequency of each zip code in the dataset

Answer

Use label encoding: assign each zip code a unique integer

Question 2

A data scientist is analyzing a dataset for a binary classification problem. The dataset has 10,000 samples and 200 features. After splitting into training (80%) and test (20%), the data scientist trains a decision tree classifier and achieves 100% accuracy on the training set but only 55% on the test set. Which step should the data scientist take first to address this issue?

Accepted Answer

Prune the decision tree to reduce complexity. The large discrepancy between training and test accuracy indicates overfitting, and pruning the decision tree (e.g., limiting max_depth) reduces overfitting. Option A is wrong because cross-validation is a technique to evaluate model performance but does not directly fix overfitting. Option B is wrong because more data may help but is not the first step; also data is limited. Option C is wrong because more features may worsen overfitting.

Answer

Use cross-validation to evaluate model performance

Answer

Collect more training data

Answer

Add more features to the model

Question 3

Which TWO actions are appropriate when handling missing data in a dataset for machine learning? (Select TWO.)

Accepted Answer

Use a machine learning model to predict missing values based on other features. Options A and C are correct. Using a machine learning model to predict missing values is a valid imputation technique that can preserve relationships in the data. Imputing with the mean or median is a standard approach for numerical features and maintains the dataset size. Option B is incorrect because dropping all rows with any missing values can lead to significant data loss, especially if missingness is widespread. Option D is incorrect because removing an entire feature due to missing values might discard predictive information unless the feature is mostly missing. Option E is incorrect because filling all missing values with zero can introduce bias and distort distributions, as zero may not be a natural placeholder for the data.

Answer

Drop all rows that contain any missing value

Answer

Remove the feature entirely if it contains missing values

Answer

Fill missing values with zero

Question 4

Which THREE techniques are commonly used to detect outliers in a dataset? (Select THREE.)

Accepted Answer

Interquartile range (IQR). Options A, D, and E are correct. Z-score and IQR are standard statistical methods for identifying outliers. Isolation Forest is a machine learning algorithm specifically designed for anomaly detection. Option B (k-means clustering) is incorrect because it is a clustering algorithm, not typically used for outlier detection. Option C (PCA) is incorrect because principal component analysis is used for dimensionality reduction, though it can be used in some outlier detection contexts, it is not one of the three most common techniques.

Answer

k-means clustering

Answer

Principal component analysis (PCA)

Question 5

Which THREE statements about data leakage in machine learning are correct? (Select THREE.)

Accepted Answer

Using the target variable to filter features before splitting leads to data leakage. Using the target variable to filter features before splitting allows test set information to influence feature selection, causing data leakage. Option B is correct: applying SMOTE after splitting the dataset into training and test sets prevents leakage that would occur if SMOTE were applied before splitting, as synthetic samples would then be generated using information from the entire dataset. While SMOTE after splitting does not prevent all forms of leakage, the statement 'prevents data leakage' is interpreted in the context of the specific leakage that SMOTE can introduce, making it a correct practice. Option C is correct: standardizing the entire dataset before splitting uses statistics computed from both training and test data, which leaks information about the test set into the training process. Option D is incorrect because cross-validation does not eliminate all leakage; if preprocessing steps like scaling are applied to the entire dataset before cross-validation, leakage still occurs. Option E is incorrect because for time series data, a random train-test split ignores the temporal order and can cause future information to leak into past predictions; a time-based split is recommended.

Answer

Using cross-validation eliminates all possible data leakage

Answer

For time series data, using a random train-test split is recommended to avoid data leakage

Question 6

A data scientist needs to understand the distribution of a continuous variable in a large dataset stored in Amazon S3. Which AWS service is most appropriate for quickly generating summary statistics and visualizations?

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a business analytics service that easily connects to S3 data to create interactive visualizations, dashboards, and summary statistics like histograms, making it ideal for this task. Amazon Athena is a query service for running SQL on S3, but it does not generate visualizations. Amazon SageMaker Studio is a machine learning IDE for building and training models, not for quick ad-hoc analysis. AWS Glue is a serverless data integration service for ETL, not for analysis or visualization.

Answer

AWS Glue

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Question 7

A data scientist is analyzing a dataset with missing values. The missing data is not random and is correlated with other features. Which imputation method is most appropriate to minimize bias?

Accepted Answer

Multiple imputation using MICE. Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because last observation carried forward is only appropriate for time series data where missing values are filled with the previous observation; it does not handle non-random missing data correlated with other features. Option C is wrong because listwise deletion reduces sample size and may introduce bias when data is not missing completely at random. Option D is wrong because mean imputation can bias estimates and reduce variability, especially when missingness is related to other features.

Answer

Last observation carried forward

Answer

Listwise deletion

Answer

Mean imputation

Question 8

A data scientist is performing feature engineering on a dataset with high cardinality categorical features (e.g., ZIP codes with thousands of unique values). Which technique is most effective for reducing dimensionality while preserving predictive power?

Accepted Answer

Target encoding. Target encoding (also known as mean encoding) replaces each category with the mean of the target variable for that category. This preserves the predictive signal by directly encoding the relationship between the category and the target, while reducing the dimensionality to a single continuous feature. Hash encoding can cause collisions and loss of information. One-hot encoding creates too many dummy variables for high cardinality features. Label encoding imposes an arbitrary ordinal relationship that may not exist and can mislead models.

Answer

Hash encoding

Answer

One-hot encoding

Answer

Label encoding

Question 9

A data scientist is using Amazon SageMaker Data Wrangler to explore a dataset. They notice that a feature has a very high correlation (0.95) with the target variable. What should they do to avoid overfitting?

Accepted Answer

Remove the feature from the dataset. The feature with a 0.95 correlation to the target is likely leaking target information (data leakage), which would cause the model to overfit on training data but fail on new data. Removing the feature (Option D) directly addresses the leakage. Option A (L2 regularization) helps with overfitting from noisy features but does not remove the leaked information. Option B (PCA) reduces dimensionality but the leak would still be present in the principal components. Option C (StandardScaler) only normalizes the feature, not removes it. Therefore, the best action is to remove the feature.

Answer

Use L2 regularization in the model

Answer

Apply PCA to reduce dimensionality

Answer

Standardize the feature using StandardScaler

Question 10

A data scientist needs to detect outliers in a dataset with multiple features that follow different distributions. Which method is most robust for multivariate outlier detection?

Accepted Answer

Isolation Forest. Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming any specific distribution. Option A is wrong because Z-score assumes a normal distribution. Option B is wrong because IQR is univariate and does not capture multivariate interactions. Option C is wrong because DBSCAN is primarily a clustering algorithm and is not specifically designed for outlier detection, though it can identify outliers as noise; however, Isolation Forest is more robust for this purpose.

Answer

Z-score threshold

Answer

Interquartile range (IQR)

Answer

DBSCAN clustering

Question 11

A data scientist is exploring a dataset containing customer transactions. They want to create a feature that captures the average purchase amount per customer over the last 30 days. Which approach is most efficient in Amazon SageMaker Processing?

Accepted Answer

Use PySpark with window functions in SageMaker Processing. Using PySpark with window functions in SageMaker Processing allows efficient distributed computation for grouped time-series aggregations like average purchase amount per customer over the last 30 days. Option A is wrong because Amazon Athena SQL requires moving data out of SageMaker Processing and may not be as tightly integrated. Option C is wrong because iterating over rows with a Python for loop is inefficient and does not scale. Option D is wrong because pandas groupby and rolling functions may not scale to large datasets in a distributed environment; SageMaker Processing with PySpark provides better performance.

Answer

Use Amazon Athena SQL query with GROUP BY

Answer

Use a Python script with a for loop to calculate per customer

Answer

Use pandas groupby and rolling functions

Question 12

A data scientist is working with a dataset that has imbalanced classes (1% positive). They want to explore the data before modeling. Which visualization technique is most appropriate to understand the distribution of features with respect to the target class?

Accepted Answer

Parallel coordinates plot. Parallel coordinates plot can show feature patterns for minority vs majority class in high dimensions. Option A is wrong because box plots are univariate and do not show interactions between features. Option C is wrong because histograms are univariate and do not show interaction. Option D is wrong because scatter plot matrices become cluttered with many features.

Answer

Box plots grouped by class

Answer

Histograms overlaid by class

Answer

Scatter plot matrix

Question 13

A data scientist wants to understand the statistical relationship between two categorical variables in a dataset. Which test is most appropriate?

Accepted Answer

Chi-squared test. The chi-squared test is used to determine if there is a significant association between two categorical variables, which is exactly what the data scientist wants to understand. Option B (Pearson correlation coefficient) is incorrect because it measures linear relationship between two continuous variables. Option C (Student's t-test) is used to compare means of two groups, typically for continuous data. Option D (ANOVA) is used to compare means across three or more groups, also for continuous data.

Answer

Pearson correlation coefficient

Answer

Student's t-test

Answer

ANOVA test

Question 14

A data scientist is analyzing a dataset with a time series component. They suspect there is a weekly seasonality. Which technique should they use to confirm this?

Accepted Answer

Compute autocorrelation function (ACF). The autocorrelation function (ACF) measures the correlation between a time series and its lagged values. A significant spike at lag 7 confirms weekly seasonality. Option A (line chart) is subjective and not a definitive test. Option C (Fourier transform) identifies frequency components but is more complex and less direct for confirming seasonality. Option D (moving average) smooths the series and may obscure seasonality.

Answer

Plot the time series line chart

Answer

Perform Fourier transform

Answer

Compute a 7-day moving average

Question 15

A data scientist is exploring a dataset with skewed numerical features. Which THREE transformations can help make the features more normally distributed?

Accepted Answer

Yeo-Johnson transformation. Correct options: C, D, E. Yeo-Johnson transformation (C), Box-Cox transformation (D), and log transformation (E) are all effective for making skewed numerical features more normally distributed. Option A, min-max scaling, only rescales the feature to a fixed range and does not change the distribution shape. Option B, standardization (Z-score), centers and scales the data but does not alter skewness.

Answer

Min-max scaling

Answer

Standardization (Z-score)