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

Question 1

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. Option A is correct because Amazon QuickSight integrates with S3 and can generate histograms and summary statistics without needing to move data. Option B is wrong because SageMaker is for model building, not quick ad-hoc analysis. Option C is wrong because Athena is for querying, not visualization. Option D is wrong because Glue is for ETL, not analysis.

Answer

AWS Glue

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Question 2

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. Option B is correct because Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because mean imputation can bias estimates when data is not missing completely at random. Option C is wrong because dropping rows reduces sample size and may introduce bias. Option D is wrong because last observation carried forward is for time series.

Answer

Last observation carried forward

Answer

Listwise deletion

Answer

Mean imputation

Question 3

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. Option D is correct because target encoding (mean encoding) replaces categories with the mean of the target variable, which captures predictive signal and reduces cardinality. Option A is wrong because one-hot encoding creates many columns, leading to high dimensionality. Option B is wrong because label encoding implies ordinality that may not exist. Option C is wrong because hashing can cause collisions and loss of information.

Answer

Hash encoding

Answer

One-hot encoding

Answer

Label encoding

Question 4

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. Option A is correct because a feature with correlation 0.95 may be directly derived from the target or leaking information; removing it prevents overfitting. Option B is wrong because PCA is for dimensionality reduction but may still include the leak. Option C is wrong because regularization helps but may not fully address leakage. Option D is wrong because scaling does not address correlation.

Answer

Use L2 regularization in the model

Answer

Apply PCA to reduce dimensionality

Answer

Standardize the feature using StandardScaler

Question 5

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. Option C is correct because Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming distribution. Option A is wrong because Z-score assumes normal distribution. Option B is wrong because IQR is univariate. Option D is wrong because DBSCAN is for clustering, not specifically for outlier detection.

Answer

Z-score threshold

Answer

Interquartile range (IQR)

Answer

DBSCAN clustering

Question 6

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. Option D is correct because using PySpark in SageMaker Processing with window functions is efficient for grouped time-series aggregations. Option A is wrong because iterating over rows is inefficient in Python. Option B is wrong because SQL in Athena may be simpler but requires moving data. Option C is wrong because pandas may not scale to large datasets.

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 7

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

Answer

Box plots grouped by class

Answer

Histograms overlaid by class

Answer

Scatter plot matrix

Question 8

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. Option B is correct because the chi-squared test tests independence between categorical variables. Option A is wrong because ANOVA is for continuous vs categorical. Option C is wrong because Pearson correlation is for continuous variables. Option D is wrong because t-test compares means of two groups.

Answer

Pearson correlation coefficient

Answer

Student's t-test

Answer

ANOVA test

Question 9

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). Option C is correct because autocorrelation function (ACF) can show peaks at lag 7 indicating weekly seasonality. Option A is wrong because line plot can show patterns but is subjective. Option B is wrong because moving average smooths data and may hide seasonality. Option D is wrong because spectral analysis is for frequency, but ACF is simpler.

Answer

Plot the time series line chart

Answer

Perform Fourier transform

Answer

Compute a 7-day moving average

Question 10

A data scientist is performing exploratory data analysis on a dataset with missing values. Which TWO approaches are appropriate for handling missing data in a way that retains as much data as possible?

Accepted Answer

Use a model that handles missing values natively, such as XGBoost. Correct options: A and C. Option A (imputation) retains all rows and fills missing values. Option C (use algorithms that handle missing values) avoids deletion. Option B is wrong because dropping rows reduces data. Option D is wrong because dropping columns loses features. Option E is wrong because replacing with a constant may bias.

Answer

Replace missing values with a constant like -999

Answer

Drop columns with high missing percentage

Answer

Drop rows with any missing value

Question 11

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: A, B, D. Log transform (A), Box-Cox (B), and Yeo-Johnson (D) are common for normalizing skewed data. Option C is wrong because standardization (Z-score) does not change distribution shape. Option E is wrong because min-max scaling does not fix skewness.

Answer

Min-max scaling

Answer

Standardization (Z-score)

Question 12

A data scientist is analyzing a dataset with high multicollinearity. Which TWO techniques can help identify and address multicollinearity?

Accepted Answer

Use Principal Component Analysis (PCA). Correct options: A and D. VIF (A) quantifies multicollinearity; PCA (D) creates orthogonal components. Option B is wrong because correlation matrix only shows pairwise correlations. Option C is wrong because Lasso does feature selection but does not identify multicollinearity. Option E is wrong because RFE is for feature selection, not multicollinearity detection.

Answer

Plot a correlation matrix

Answer

Apply Lasso regression

Answer

Use Recursive Feature Elimination (RFE)

Question 13

A data scientist ran an AWS Glue ETL job that failed with the error shown. What is the most likely cause?

Accepted Answer

The DataFrame does not have a column named 'age'. Option C is correct because the error indicates the column 'age' is not found in the input data, which has columns [id, name, salary]. Option A is wrong because the error is about a missing column, not a mismatch. Option B is wrong because schema evolution would add column, not cause error. Option D is wrong because there is no indication of data type issue.

Answer

The CSV file has a header mismatch

Answer

The schema is evolving incorrectly

Answer

The data type of 'age' is incompatible

Question 14

A data scientist queried an Athena table and got only one row back, but the CSV file is 1 MB. What is the most likely reason?

Accepted Answer

The table is partitioned but the partition is not correctly defined. Option B is correct because the file is large but the query returned only one row, suggesting the table's partition mapping is wrong; the WHERE clause on date may not match the actual partition. Option A is wrong because if the table were external, it would still read all data. Option C is wrong because 1 MB file likely has many rows. Option D is wrong because Athena supports CSV.

Answer

The CSV file contains only one row

Answer

The table is not an external table

Answer

Athena does not support CSV format

Question 15

A data scientist is working on a predictive maintenance project for a manufacturing company. Sensor data is collected every second from 100 machines and stored in an Amazon S3 bucket as Parquet files, partitioned by machine_id and date. The dataset is massive (10 TB) and contains over 2000 features per machine. The data scientist needs to perform exploratory data analysis to identify which features are most predictive of machine failure. They have access to Amazon SageMaker Studio with a SageMaker Data Wrangler flow. The initial data exploration is taking too long due to the volume of data. The data scientist wants to speed up the analysis without losing accuracy in feature selection. Which course of action is most appropriate?

Accepted Answer

Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample. Option B is correct because using SageMaker Data Wrangler's sampling capabilities allows faster exploration while preserving statistical properties for feature selection. Option A is wrong because reducing to a single partition loses time series context. Option C is wrong because moving to a smaller instance may cause memory issues. Option D is wrong because random sample of rows may break time series ordering.

Answer

Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset

Answer

Reduce the data to a single partition by concatenating all files and use only one machine's data

Answer

Use Amazon Athena to query a random sample of rows from the dataset