MLS-C01 Exploratory Data Analysis Practice Test 25 — 15 Questions

Question 1

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: D and E. Variance Inflation Factor (VIF) (E) is a key metric for detecting multicollinearity by measuring how much the variance of a coefficient increases due to collinearity. PCA (D) addresses multicollinearity by transforming correlated features into orthogonal components. Option A is incorrect because a correlation matrix only shows pairwise correlations and may miss higher-order multicollinearity. Option B is incorrect because Lasso regression performs feature selection by shrinking coefficients but does not directly identify multicollinearity. Option C is incorrect because Recursive Feature Elimination (RFE) is a feature selection method that does not detect multicollinearity.

Answer

Plot a correlation matrix

Answer

Apply Lasso regression

Answer

Use Recursive Feature Elimination (RFE)

Question 2

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'. The error message indicates that the column 'age' is not found in the DataFrame, which only contains columns [id, name, salary]. Option A is incorrect because the error is about a missing column, not a header mismatch. Option C is incorrect because schema evolution would add a column, not cause a missing column error. Option D is incorrect because there is no indication of a data type issue; the error is about column existence.

Answer

The CSV file has a header mismatch

Answer

The schema is evolving incorrectly

Answer

The data type of 'age' is incompatible

Question 3

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. A 1 MB CSV file likely contains many rows, but querying returns only one row, which indicates that the table's partition mapping is incorrect. Athena uses partitions to minimize data scanned; if the partition definition does not match the actual data location, the query may only read (or miss) certain partitions, resulting in fewer rows. Option B is wrong because a 1 MB file is too large to contain only one row, as typical CSV rows are much smaller. Option C is irrelevant: whether the table is external does not affect row count. Option D is wrong because Athena supports CSV format.

Answer

The CSV file contains only one row

Answer

The table is not an external table

Answer

Athena does not support CSV format

Question 4

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. SageMaker Data Wrangler supports stratified sampling, which preserves the distribution of machine failure across machine_id and date, allowing for faster exploratory data analysis while maintaining representativeness for feature selection. Option A is incorrect because distributed processing with EMR on the full dataset may still be slow and is unnecessary when sampling can capture the signal. Option B is incorrect because using only one machine's data loses cross-machine variability and may bias feature selection. Option D is incorrect because random sampling does not guarantee preservation of time series order or failure distribution, potentially compromising analysis accuracy.

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

Question 5

A data scientist wants to understand the distribution of a continuous feature before training a model. Which visualization is most appropriate?

Accepted Answer

Histogram. A histogram is the standard tool for showing the distribution of a single continuous variable. Option A is wrong because scatter plots compare two variables. Option B is wrong because box plots show summary statistics, not the full distribution shape. Option D is wrong because bar charts are for categorical data.

Answer

Scatter plot

Answer

Box plot

Answer

Bar chart

Question 6

A data scientist runs a logistic regression and obtains a model with 95% accuracy on the training set. However, the model performs poorly on the test set. Which exploratory data analysis step should have been performed to identify this issue?

Accepted Answer

Checking for class imbalance in the target variable. Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A (correlation matrix) is wrong because it helps with multicollinearity, not class imbalance. Option B (log transformation) is wrong because it addresses skewness in features, not class imbalance. Option D (heatmap of missing values) is wrong because it shows missing data patterns, not class imbalance.

Answer

Generating a correlation matrix of features

Answer

Log transformation of skewed features

Answer

Creating a heatmap of missing values

Question 7

A data scientist is analyzing a dataset with many features and wants to identify which features are most correlated with the target variable. Which EDA technique should be used?

Accepted Answer

Correlation matrix. A correlation matrix displays pairwise Pearson correlation coefficients between all numeric features and the target variable, enabling quick identification of the most correlated features. Option A (box plots grouped by target) is useful for visualizing feature distributions across target categories but does not directly measure correlation strength. Option B (scatter plot matrix) can show pairwise relationships but becomes impractical with many features and lacks a single quantitative correlation measure. Option C (histogram) only shows the distribution of a single feature, not its relationship with the target. Therefore, the correlation matrix is the appropriate EDA technique for identifying features most correlated with the target.

Answer

Box plots grouped by target

Answer

Scatter plot matrix

Answer

Histogram of each feature

Question 8

A data scientist is performing EDA on a time series dataset of daily sales. The data scientist observes a pattern that repeats every 7 days. Which characteristic of the time series is being observed?

Accepted Answer

Seasonality. A pattern that repeats at a fixed frequency (every 7 days) is called seasonality. Option A is wrong because trend is a long-term increase or decrease. Option C is wrong because autocorrelation measures correlation with lagged values, not a repeating pattern. Option D is wrong because stationarity refers to constant mean/variance over time.

Answer

Stationarity

Answer

Autocorrelation

Answer

Trend

Question 9

During EDA, a data scientist finds that a feature has a skewness value of 2.5. What does this indicate about the data distribution?

Accepted Answer

The distribution is right-skewed. A skewness value of 2.5 is positive and greater than 1, indicating a highly right-skewed (positively skewed) distribution, where the tail extends to the right. Option A correctly identifies this. Option B is wrong because symmetric distributions have skewness near 0. Option C is wrong because left-skewed distributions have negative skewness. Option D is wrong because skewness measures asymmetry, not necessarily the presence of outliers.

Answer

The distribution is symmetric

Answer

The distribution is left-skewed

Answer

The distribution has no outliers

Question 10

A data scientist uses SageMaker Studio to run EDA on a dataset with 500 features. The goal is to reduce dimensionality before modeling. Which EDA technique should the data scientist use to understand the variance explained by each feature?

Accepted Answer

Scree plot of principal components. A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a histogram shows distribution, not variance. Option C is wrong because a heatmap of correlations shows pairwise relationships, not variance. Option D is wrong because a box plot shows summary statistics.

Answer

Histogram of the target variable

Answer

Heatmap of feature correlations

Answer

Box plot of each feature

Question 11

A data scientist is working with a dataset containing text reviews. The goal is to build a sentiment analysis model. Which EDA step is most critical before feature extraction?

Accepted Answer

Checking the distribution of sentiment labels. Checking the distribution of sentiment labels is critical before feature extraction because it reveals class imbalance, which can bias the model towards the majority class and affect evaluation metrics. This EDA step enables informed decisions about resampling or weighting techniques. Option A (vocabulary size) is not a critical first step; option B (word cloud) is a visualization tool, not essential; option C (removing stop words) is a preprocessing step, not part of EDA.

Answer

Calculating the vocabulary size

Answer

Creating a word cloud

Answer

Removing stop words

Question 12

A data scientist is performing EDA on a dataset with both numeric and categorical features. Which TWO techniques are appropriate for visualizing the relationship between a numeric feature and a binary categorical target?

Accepted Answer

Violin plot grouped by target. Correct options are C (violin plot grouped by target) and D (box plot grouped by target). Both are effective for visualizing the distribution of a numeric feature across two categories of a binary target. A violin plot combines a box plot and a density plot, showing the full distribution shape, while a box plot displays medians, quartiles, and outliers. Option A (histogram) shows distribution of a single numeric variable but does not directly compare groups. Option B (stacked bar chart) is for categorical vs categorical data. Option E (scatter plot) is for two numeric variables.

Answer

Histogram

Answer

Stacked bar chart

Answer

Scatter plot

Question 13

A data scientist is analyzing a dataset with missing values. Which THREE methods are appropriate for handling missing data during EDA and preprocessing?

Accepted Answer

Remove rows with any missing values. (remove rows with any missing values) is appropriate if missing data is random and limited. Option B (impute with mean) is commonly used for numeric features without outliers. Option E (impute with median) is robust to outliers. Option C (replace missing values with 0) is generally not recommended as it can introduce bias unless 0 is a valid value. Option D (ignore missing values and proceed with modeling) is problematic because most algorithms cannot handle missing values and will raise errors.

Answer

Replace missing values with 0

Answer

Ignore missing values and proceed with modeling

Question 14

A data scientist wants to identify outliers in a dataset. Which TWO techniques are commonly used for outlier detection during EDA?

Accepted Answer

Box plot. Box plots (A) visually identify outliers as points beyond the whiskers (typically 1.5×IQR). Z-score analysis (C) flags data points with an absolute Z-score greater than 3, indicating they are far from the mean. Heatmaps (B) show correlations between variables, not outliers. Pearson correlation (E) measures linear relationships, not outliers. Bar charts (D) display categorical frequencies and do not detect outliers.

Answer

Heatmap

Answer

Bar chart

Answer

Pearson correlation coefficient

Question 15

Refer to the exhibit. A data scientist is unable to read a CSV file from the S3 bucket 'my-bucket' using SageMaker. The IAM policy attached to the SageMaker execution role is shown. What is the most likely cause of the failure?

Accepted Answer

The bucket uses server-side encryption with AWS KMS (SSE-KMS) and the policy lacks kms:Decrypt permission. The policy includes s3:GetObject and s3:ListBucket, so it allows reading objects. However, if the S3 bucket uses server-side encryption with AWS KMS (SSE-KMS), the SageMaker execution role must also have the kms:Decrypt permission to decrypt the object. Without this permission, the read operation fails even though the S3 permissions are correct. Therefore, the most likely cause is that the bucket uses SSE-KMS and the policy lacks kms:Decrypt, making option C correct.

Answer

The policy does not allow the s3:GetObject action

Answer

The policy does not grant read access to the bucket

Answer

The policy does not include s3:ListBucket action