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

Question 1

A data scientist has a dataset with 500 features and wants to reduce dimensionality for visualization. Which technique is most appropriate for identifying the two components that capture the most variance?

Accepted Answer

Principal Component Analysis (PCA). Option A is correct because PCA is designed to find principal components that maximize variance. Option B is wrong because t-SNE is for visualization but not variance-based; it focuses on preserving local structure. Option C is wrong because LDA is supervised and requires labels. Option D is wrong because K-means is clustering, not dimensionality reduction.

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

K-means clustering

Question 2

A data engineer runs a SQL query on Amazon Athena to explore a dataset stored in S3 as CSV. The query returns zero rows for a column that should have numeric values. Which step should the engineer take to diagnose the issue?

Accepted Answer

Check the table schema in AWS Glue Data Catalog to ensure the column data type is correct.. Option B is correct because checking the schema and data type conversion can reveal issues like unquoted commas or wrong format. Option A is wrong because the issue is likely with data types, not encryption. Option C is wrong because adding a partition won't fix data type issues. Option D is wrong because crawling does not change data types if schema is inferred incorrectly.

Answer

Verify that the S3 bucket has encryption enabled.

Answer

Run an AWS Glue crawler to update the table schema.

Answer

Add a partition to the table for the date column.

Question 3

A data scientist is performing EDA on a dataset with missing values in 3 of 20 features. The missing rate is 5% for each feature. The scientist wants to preserve as much data as possible while avoiding bias. Which imputation strategy is most appropriate?

Accepted Answer

Impute missing values with the median of each feature.. Option A is correct because median imputation is robust to outliers and preserves the dataset size. Option B is wrong because dropping rows with missing values would lose 14% of data. Option C is wrong because mean imputation can be affected by outliers. Option D is wrong because KNN imputation may introduce bias and is computationally expensive.

Answer

Remove rows with any missing values.

Answer

Impute missing values with the mean of each feature.

Answer

Use K-Nearest Neighbors (KNN) imputation.

Question 4

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset and notices that the target variable is highly imbalanced. Which technique should the data scientist apply to balance the dataset before training?

Accepted Answer

Synthetic Minority Oversampling Technique (SMOTE). Option D is correct because SMOTE generates synthetic samples for the minority class. Option A is wrong because it discards majority class data. Option B is wrong because scaling does not balance classes. Option C is wrong because encoding is for categorical features.

Answer

One-hot encoding of the target variable

Answer

Random undersampling of the majority class

Answer

Min-Max scaling of all features

Question 5

During EDA, a data scientist finds that a numeric feature has many outliers. The feature will be used in a linear regression model. Which approach should the scientist take to handle the outliers?

Accepted Answer

Cap the feature values at the 1st and 99th percentiles.. Option C is correct because capping winsorizes outliers, reducing their impact while retaining data. Option A is wrong because removing all outliers may lose information. Option B is wrong because log transform reduces skew but does not remove outliers. Option D is wrong because scaling does not mitigate outlier influence.

Answer

Remove all rows with outlier values.

Answer

Apply a logarithmic transformation to the feature.

Answer

Standardize the feature using Z-score normalization.

Question 6

A data scientist examines a dataset with 100 features and suspects that some features are redundant due to high pairwise correlations. Which EDA technique should the scientist use to systematically identify groups of highly correlated features?

Accepted Answer

Generate a correlation matrix and visualize it as a heatmap.. Option B is correct because a correlation matrix heatmap visually identifies high correlations. Option A is wrong because histograms show univariate distributions. Option C is wrong because scatter plots are for pairs, not systematic. Option D is wrong because box plots show outliers.

Answer

Plot histograms for each feature.

Answer

Create scatter plots for each pair of features.

Answer

Use box plots to identify outliers.

Question 7

Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)

Accepted Answer

Calculate the percentage of missing values per column.. Options B and E are correct. B: Visualizing the distribution helps identify shape and outliers. E: Reporting missing value counts is a standard EDA step. A is wrong because imputation should be done after analysis. C is wrong because removing outliers without analysis is premature. D is wrong because normalization is not a first step.

Answer

Normalize all features using Min-Max scaling.

Answer

Remove all rows with outliers.

Answer

Impute missing values with the mean immediately.

Question 8

Which THREE techniques are commonly used in exploratory data analysis to understand the relationships between features and the target variable? (Select THREE.)

Accepted Answer

Use box plots to compare feature distributions across target classes.. Options A, C, and E are correct. A: Correlation matrix quantifies linear relationships. C: Pair plots allow visual inspection of multiple relationships. E: Box plots by target show distribution differences. B is wrong because PCA is for dimensionality reduction, not EDA of relationships. D is wrong because clustering is unsupervised and not directly for feature-target relationships.

Answer

Perform K-means clustering on the features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensions.

Question 9

Which TWO statements about handling missing data during exploratory data analysis are correct? (Select TWO.)

Accepted Answer

Visualizing the pattern of missingness can help determine if data is missing at random.. Options B and D are correct. B: Visualizing missing patterns (e.g., with a missingno matrix) is a good EDA practice. D: Understanding the mechanism (MCAR, MAR, MNAR) is critical for choosing imputation method. A is wrong because listwise deletion can introduce bias if data not MCAR. C is wrong because mean imputation reduces variance. E is wrong because missing values should be handled before model training, not after.

Answer

Missing values can be ignored during EDA and handled during model training.

Answer

Listwise deletion (removing rows with missing values) is always safe and unbiased.

Answer

Imputing missing values with the mean preserves the original variance.

Question 10

A data scientist is exploring a dataset with 10 million rows and 500 features. The target variable is binary. The dataset is stored in an Amazon S3 bucket. The data scientist wants to quickly identify which features have the highest correlation with the target variable. Which approach is MOST efficient?

Accepted Answer

Use Amazon SageMaker Data Wrangler to import the dataset from S3 and generate a correlation matrix.. Option B is correct because Amazon SageMaker Data Wrangler can connect to S3, perform correlation analysis, and generate a correlation matrix without writing code. Option A is wrong because AWS Glue ETL is for ETL pipelines, not interactive exploration. Option C is wrong because Amazon Athena is for SQL queries, not correlation analysis. Option D is wrong because Amazon QuickSight is for visualization, not statistical correlation.

Answer

Use Amazon QuickSight to create scatter plots for each feature vs. target.

Answer

Use Amazon Athena with SQL queries to compute correlation coefficients.

Answer

Use AWS Glue ETL to compute pairwise correlations and output to Amazon Redshift.

Question 11

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset has missing values in the 'age' column and outliers in the 'amount' column. Which combination of techniques should the engineer use to handle these issues during EDA?

Accepted Answer

Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.. Option A is correct because median imputation is robust to outliers, and IQR-based capping is standard for outlier handling. Option B is wrong because mean imputation is sensitive to outliers. Option C is wrong because removing rows with missing age may lose data. Option D is wrong because z-score with mean/std is also sensitive to outliers.

Answer

Remove rows with missing age and apply log transformation to 'amount'.

Answer

Impute missing age values with a constant (e.g., 0) and cap outliers using mean ± 3*std.

Answer

Impute missing age values with the mean and remove outliers in 'amount' using z-score.

Question 12

A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?

Accepted Answer

Calculate mutual information scores between each feature and the target.. Option D is correct because mutual information captures any kind of dependency, including non-linear. Option A is wrong because Pearson correlation only measures linear relationships. Option B is wrong because PCA is for dimensionality reduction, not feature-target relationship. Option C is wrong because correlation matrix is pairwise among features, not with target.

Answer

Visualize the correlation matrix heatmap of all features.

Answer

Apply Principal Component Analysis (PCA) and examine the loadings.

Answer

Compute Pearson correlation coefficients between each feature and the target.

Question 13

A company is preparing a dataset for training a binary classification model. The dataset has a severe class imbalance (1% positive class). The data scientist wants to understand the impact of this imbalance on model performance before sampling. Which exploratory analysis step is MOST critical?

Accepted Answer

Plot the distribution of each feature separately for the positive and negative classes.. Option B is correct because analyzing the distribution of features across classes can reveal separability and potential issues. Option A is wrong because correlation with target is not the primary concern. Option C is wrong because missing values are not the immediate concern. Option D is wrong because PCA is not necessary at this stage.

Answer

Compute the correlation matrix of all features with the target variable.

Answer

Check for missing values and outliers in the dataset.

Answer

Perform PCA and visualize the first two principal components colored by class.

Question 14

A data scientist is working with a dataset that contains text reviews and a numeric rating (1-5). The goal is to predict the rating from the review text. During EDA, the scientist wants to check if there are any spelling errors or unusual characters. Which tool is BEST suited for this task?

Accepted Answer

Amazon Comprehend to detect syntax and entities.. Option C is correct because Amazon Comprehend can detect entities, key phrases, and syntax, but not spelling errors directly; however, it can be used to identify unusual patterns. Actually, for spelling errors, a custom solution may be needed. But among options, Comprehend is the only AWS AI service that processes text. Option A is wrong because SageMaker Data Wrangler is for tabular data. Option B is wrong because Athena is for SQL. Option D is wrong because QuickSight is for visualization.

Answer

Amazon SageMaker Data Wrangler with a custom transform for text cleaning.

Answer

Amazon Athena with SQL queries to find anomalies.

Answer

Amazon QuickSight to create word clouds.

Question 15

A data engineer is performing EDA on a time-series dataset of server metrics (CPU, memory, disk I/O) collected every minute. The dataset contains 2 years of data. The engineer suspects there are seasonal patterns and wants to decompose the time series for one metric. Which AWS service can be used to perform this decomposition natively?

Accepted Answer

Amazon SageMaker Canvas. Option A is correct because Amazon SageMaker Canvas supports time-series forecasting with built-in decomposition. Option B is wrong because AWS Glue does not have time-series decomposition. Option C is wrong because Amazon QuickSight does not decompose time series. Option D is wrong because Amazon Athena does not have decomposition functions.

Answer

Amazon Athena with SQL window functions

Answer

Amazon QuickSight

Answer

AWS Glue DataBrew