MLS-C01 Exploratory Data Analysis Practice Test 16 — 15 Questions

Question 1

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?

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

Accepted Answer

Impute missing values with the median of each feature.. Median imputation (Option D) is the most appropriate because it preserves the dataset size, is robust to outliers, and avoids bias introduced by more complex methods. Removing rows (Option A) would discard approximately 14% of the data if missing patterns are independent, unnecessarily reducing sample size. Mean imputation (Option B) is sensitive to outliers, which could skew the distribution. KNN imputation (Option C) may introduce bias if the neighborhood size is not properly tuned and is computationally expensive for large datasets. Therefore, median imputation provides a simple, robust solution that maintains data integrity.

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 2

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). Synthetic Minority Oversampling Technique (SMOTE) is the correct technique because it generates synthetic samples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, effectively balancing the dataset without simply duplicating data. Amazon SageMaker Data Wrangler includes a built-in SMOTE transform, making it directly applicable for handling imbalanced target variables during exploratory data analysis.

Answer

One-hot encoding of the target variable

Answer

Random undersampling of the majority class

Answer

Min-Max scaling of all features

Question 3

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.. Capping (winsorizing) the feature values at the 1st and 99th percentiles limits the influence of extreme outliers while retaining all data points. This is particularly important for linear regression, which is sensitive to outliers. Option A is wrong because removing all rows with outliers can lead to significant data loss and bias. Option B is wrong because a logarithmic transformation reduces skew but does not eliminate the impact of outliers; it only compresses their range. Option C is wrong because Z-score normalization standardizes the data but does not reduce the influence of outliers; extreme values remain extreme relative to the distribution.

Answer

Remove all rows with outlier values.

Answer

Apply a logarithmic transformation to the feature.

Answer

Standardize the feature using Z-score normalization.

Question 4

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.. A correlation matrix heatmap allows systematic identification of groups of highly correlated features by visually highlighting high pairwise correlations. Option B is incorrect because histograms show univariate distributions, not relationships between features. Option C is incorrect because scatter plots for each pair would be time-consuming and not systematic for 100 features. Option D is incorrect because box plots show outliers, not correlations.

Answer

Plot histograms for each feature.

Answer

Create scatter plots for each pair of features.

Answer

Use box plots to identify outliers.

Question 5

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.. Calculating the percentage of missing values per column is a standard first step in exploratory data analysis (EDA) to quantify data completeness. This informs downstream decisions such as whether to impute, drop, or flag missing data, and helps assess the risk of bias or information loss. It is a diagnostic action, not a transformation, and should precede any imputation or removal.

Answer

Normalize all features using Min-Max scaling.

Answer

Remove all rows with outliers.

Answer

Impute missing values with the mean immediately.

Question 6

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 D are correct. Box plots (A) are useful for comparing feature distributions across different target classes, revealing differences that may indicate predictive power. Scatter plots or pair plots (D) allow visual inspection of relationships between features and the target, highlighting patterns, clusters, or outliers. A correlation matrix (C) quantifies linear relationships between features and the target variable, helping identify strongly correlated features. B is incorrect because K-means clustering is an unsupervised technique used for grouping data, not for understanding feature-target relationships. E is incorrect because PCA is a dimensionality reduction technique, not a direct method for analyzing relationships between features and a target variable.

Answer

Perform K-means clustering on the features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensions.

Question 7

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 C are correct. Visualizing the pattern of missingness helps determine if data is missing at random, which is a key EDA step. Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for selecting an appropriate imputation strategy. Option A is incorrect because missing values should be addressed during EDA, not deferred to model training. Option D is incorrect because listwise deletion can introduce bias if data is not MCAR. Option E is incorrect because mean imputation reduces the variance of the imputed variable.

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 8

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.. Amazon SageMaker Data Wrangler can directly import the dataset from S3 and generate a correlation matrix efficiently without needing to write custom code, making it the most efficient approach for identifying feature correlations with the target variable. Option B is incorrect because using Amazon QuickSight to create scatter plots for each of the 500 features would be time-consuming and not scalable. Option C is incorrect because Amazon Athena uses SQL queries which are not designed to compute correlation coefficients efficiently across a large number of features. Option D is incorrect because AWS Glue ETL is intended for data transformation pipelines and is not suitable for quick interactive correlation analysis.

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 9

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.. Median imputation is robust to outliers, and IQR-based capping is a standard method for handling outliers. Option B is wrong because removing rows with missing age can lead to data loss, and log transformation reduces skewness but does not handle outliers by capping. Option C is wrong because imputing with a constant like 0 is arbitrary and can bias the data, and capping using mean ± 3*std is sensitive to outliers. Option D is wrong because mean imputation is sensitive to outliers, and removing outliers via z-score can discard valid data points.

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 10

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.. Mutual information captures any kind of dependency, including non-linear. Option A is wrong because a correlation matrix heatmap only shows pairwise linear correlations among features, not feature-target relationships. Option B is wrong because PCA is for dimensionality reduction and does not directly assess feature-target relationships. Option D is wrong because Pearson correlation only measures linear relationships, missing non-linear ones.

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 11

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.. The most critical step because plotting the distribution of each feature separately for the positive and negative classes allows the data scientist to visually assess class separability, overlap, and feature behavior under severe imbalance. This insight directly informs the impact of imbalance on model performance before any sampling. Options A, B, and C are less critical at this stage: correlation with the target (A) does not reveal class-level distributions; missing values and outliers (B) are important but not specific to understanding imbalance impact; PCA (C) is a dimensionality reduction technique that may obscure per-feature patterns and is not necessary for initial exploratory analysis of class distributions.

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 12

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.. Amazon Comprehend is the best choice among the options because it is an AWS AI service that can detect syntax, entities, and key phrases in text. While it does not directly find spelling errors, it can identify unusual patterns or anomalies in text that may indicate misspellings or odd characters. SageMaker Data Wrangler is for tabular data, Athena is for SQL queries, and QuickSight is for visualization, none of which are specialized for text analysis in this context.

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 13

A data scientist is exploring a dataset with 100 features. After generating pair plots, the scientist notices that many features have skewed distributions. Which transformation should the scientist apply to make the distributions more Gaussian-like for modeling?

Accepted Answer

Yeo-Johnson transformation. (Yeo-Johnson transformation) is correct because it can handle both positive and negative values, making it suitable for datasets with skewed distributions that may include negative numbers. Option A (Log transformation) is wrong because it only works for strictly positive values. Option C (Standard scaling) does not change the shape of the distribution; it only centers and scales the data, so it does not fix skewness. Option D (Box-Cox transformation) also requires positive values, limiting its applicability.

Answer

Log transformation

Answer

Standard scaling (z-score normalization)

Answer

Box-Cox transformation

Question 14

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?

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

Accepted Answer

Amazon Athena. Amazon Athena can query data in S3 directly using SQL. Option A is wrong because AWS Glue ETL requires job setup. Option C is wrong because Amazon SageMaker Data Wrangler requires a notebook instance. Option D is wrong because QuickSight is for visualization, not direct summary statistics.

Answer

AWS Glue ETL

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight

Question 15

A data scientist is analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method is MOST appropriate?

Accepted Answer

Multiple imputation by chained equations (MICE). MICE uses multiple imputation based on other features, accounting for the correlation between the missing feature and another feature. Option A is wrong because median imputation ignores the correlation and simply fills with the median, which does not leverage relationships between features. Option B is wrong because listwise deletion removes rows with missing data, which reduces sample size and can introduce bias if missingness is not completely random. Option C is wrong because mean imputation, like median imputation, ignores correlations and can distort relationships.

Answer

Median imputation

Answer

Listwise deletion (remove rows with missing values)

Answer

Mean imputation