MLS-C01 Exploratory Data Analysis Practice Test 4 — 15 Questions

Question 1

A data scientist is working with a dataset containing geospatial coordinates (latitude and longitude) of customer locations. The scientist wants to engineer features such as distance to the nearest store, and cluster customers into regions. Which AWS service is best suited for performing geospatial analysis and clustering during exploratory data analysis?

Accepted Answer

Amazon SageMaker with custom Python scripts using scikit-learn and Geopy. Option B is correct because Amazon SageMaker provides built-in algorithms like K-Means for clustering, and the scientist can use custom code with libraries like Geopy to compute distances. Option A is wrong because Amazon Athena with PostGIS is for querying geospatial data, not for clustering. Option C is wrong because Amazon Location Service is for maps and location tracking, not for analytical clustering. Option D is wrong because AWS Glue is for ETL, not for analysis and clustering.

Answer

Amazon Athena with PostGIS extensions

Answer

AWS Glue with geospatial transforms

Answer

Amazon Location Service

Question 2

A machine learning engineer is analyzing a dataset with a mix of categorical and numerical features. The engineer wants to understand the correlation between categorical features and the target variable. Which statistical test is most appropriate for measuring association between a categorical feature and a binary target?

Accepted Answer

Chi-squared test of independence. Option C is correct because the Chi-squared test of independence is used to determine if there is a significant association between two categorical variables, which is applicable here. Option A is wrong because Pearson correlation is for continuous variables. Option B is wrong because ANOVA is for comparing means across groups, but assumes continuous target. Option D is wrong because Mutual Information can be used but is not a statistical test with a p-value.

Answer

Pearson correlation coefficient

Answer

ANOVA (Analysis of Variance)

Answer

Mutual information

Question 3

A data analyst is exploring a dataset and wants to identify outliers in a numerical feature. Which visualization technique is most effective for detecting outliers?

Accepted Answer

Box plot. Option A is correct because a box plot explicitly shows quartiles and potential outliers as points beyond the whiskers. Option B is wrong because a histogram shows distribution but outliers may be in low-frequency bins. Option C is wrong because a scatter plot shows relationship between two variables, not univariate outliers. Option D is wrong because a line chart is for time series.

Answer

Line chart

Answer

Histogram

Answer

Scatter plot

Question 4

A team is performing exploratory data analysis on a dataset containing 10 million records stored in Amazon S3. They want to sample the data efficiently to build a representative subset for initial modeling. Which sampling method should they use to minimize bias and ensure the sample reflects the population distribution?

Accepted Answer

Stratified random sampling. Option D is correct because stratified random sampling ensures that each subgroup (stratum) is proportionally represented, which is important for imbalanced data. Option A is wrong because simple random sampling may miss rare subgroups. Option B is wrong because systematic sampling can introduce bias if there is periodicity. Option C is wrong because reservoir sampling is for streaming data, not for static datasets.

Answer

Simple random sampling

Answer

Systematic sampling

Answer

Reservoir sampling

Question 5

A data scientist is using Amazon SageMaker to perform exploratory data analysis on a dataset with missing values and outliers. Which TWO actions should the scientist take to understand the data quality? (Choose TWO.)

Accepted Answer

Use histograms to visualize the distribution of each numerical feature. Option A is correct because generating summary statistics helps identify missing counts and outliers via min/max. Option D is correct because visualizing distributions with histograms helps spot outliers and skewness. Option B is wrong because a correlation matrix does not directly show missing values or outliers. Option C is wrong because a confusion matrix is for classification models, not for data exploration. Option E is wrong because a scatterplot matrix shows pairwise relationships, not missing values.

Answer

Build a scatterplot matrix to visualize pairwise relationships

Answer

Plot a confusion matrix to assess class separation

Answer

Create a correlation matrix to identify redundant features

Question 6

A machine learning engineer is analyzing a dataset with a large number of features (p >> n). The engineer suspects that many features are irrelevant. Which THREE methods are suitable for feature selection during exploratory data analysis? (Choose THREE.)

Accepted Answer

Remove features with variance below a threshold (e.g., <0.01). Option A is correct because correlation-based feature selection removes highly correlated features. Option C is correct because mutual information measures relevance to the target. Option E is correct because Variance Threshold removes low-variance features. Option B is wrong because PCA is a dimensionality reduction technique, not a feature selection method. Option D is wrong because Lasso regression is a modeling technique, not typically used in EDA.

Answer

Fit a Lasso regression model and select features with non-zero coefficients

Answer

Apply Principal Component Analysis (PCA) and select top components

Question 7

A data analyst is using AWS Glue to catalog datasets for exploratory analysis. The analyst wants to understand the schema and data types. Which TWO tools can the analyst use to view the schema of a table in the AWS Glue Data Catalog? (Choose TWO.)

Accepted Answer

Amazon Athena. Option B is correct because Athena can query the INFORMATION_SCHEMA to view table schemas. Option D is correct because the Glue console displays table schemas directly. Option A is wrong because QuickSight is a visualization tool, not for schema viewing. Option C is wrong because S3 does not store schemas; it stores objects. Option E is wrong because Redshift returns data, not schema from Glue Catalog directly.

Answer

Amazon Redshift query editor

Answer

Amazon QuickSight

Answer

Amazon S3 console

Question 8

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

Accepted Answer

Train a Random Forest classifier and use feature_importances_. Option A is correct because Random Forest feature importance is a well-known method for ranking features in classification tasks. Option B is wrong because PCA is unsupervised and does not use the target. Option C is wrong because K-means is clustering, not feature selection. Option D is wrong because t-SNE is for visualization, not feature importance.

Answer

Use t-SNE to reduce dimensionality and inspect clusters

Answer

Run K-means clustering and examine cluster centroids

Answer

Apply PCA and select components with highest variance

Question 9

A company has a dataset with a large number of missing values in several columns. The data scientist wants to impute missing values without introducing bias. Which approach should be used?

Accepted Answer

Use iterative imputation (MICE) to model missing values. Option C is correct because MICE (Multiple Imputation by Chained Equations) is a sophisticated method that models each variable with missing values as a function of other variables, reducing bias. Option A is wrong because mean imputation can reduce variance and bias relationships. Option B is wrong because dropping rows loses data. Option D is wrong because mode imputation for categorical data may introduce bias if missingness is not random.

Answer

Remove rows with any missing values

Answer

Replace missing values with the mode of each column

Answer

Replace missing values with the mean of each column

Question 10

During exploratory data analysis on a dataset with 1 million rows, a data scientist notices that the distribution of the target variable is highly imbalanced (99% class A, 1% class B). Which technique should be applied to address this imbalance before model training?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class, balancing the dataset. Option A is wrong because random undersampling can discard important data. Option B is wrong because scaling does not address imbalance. Option C is wrong because PCA does not fix imbalance.

Answer

Randomly undersample the majority class to match the minority class size

Answer

Apply standard scaling to all features

Answer

Use PCA to reduce dimensionality and oversample in principal component space

Question 11

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

Accepted Answer

Box plot of the feature grouped by target class. Option B is correct because a box plot shows distribution of the continuous feature across categories of the binary target, highlighting differences. Option A is wrong because a scatter plot is for two continuous variables. Option C is wrong because a histogram shows distribution of a single variable. Option D is wrong because a bar chart is for categorical vs categorical or counts.

Answer

Scatter plot with feature on x-axis and target on y-axis

Answer

Histogram of the feature

Answer

Bar chart of target class counts

Question 12

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

Accepted Answer

Remove the feature from the dataset. Option C is correct because zero variance means the feature is constant and provides no information for modeling; it should be removed. Option A is wrong because scaling does not change constant values. Option B is wrong because imputation is for missing values, not constant. Option D is wrong because interaction with a constant feature remains constant.

Answer

Create interaction terms with other features

Answer

Apply Min-Max scaling to normalize the feature

Answer

Impute missing values using the mean

Question 13

A company stores customer transaction data in Amazon S3. A data scientist needs to perform exploratory data analysis using Amazon SageMaker. The dataset is 500 GB in CSV format. Which approach is most cost-effective and time-efficient for initial data profiling?

Accepted Answer

Use Amazon S3 Select to sample rows directly from S3. Option D is correct because Amazon S3 Select can query a subset of rows from S3 without loading the entire dataset, enabling quick profiling. Option A is wrong because loading full data is expensive and slow. Option B is wrong because Glue ETL processes full dataset. Option C is wrong because converting to Parquet adds overhead for initial profiling.

Answer

Load the entire dataset into a SageMaker notebook instance and use pandas

Answer

Convert the data to Parquet format and then use Athena to query

Answer

Use AWS Glue ETL to transform the data and then analyze in Athena

Question 14

A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?

Accepted Answer

Log transformation (natural log). Option A is correct because log transformation is commonly used to reduce right skewness. Option B is wrong because square transformation increases skewness. Option C is wrong because Min-Max scaling does not change shape. Option D is wrong because one-hot encoding is for categorical features.

Answer

Min-Max scaling

Answer

One-hot encoding

Answer

Square transformation

Question 15

During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?

Accepted Answer

Multicollinearity will make coefficient estimates unstable. Option B is correct because high correlation indicates multicollinearity, which can destabilize coefficient estimates in linear regression. Option A is wrong because overfitting is not directly caused by correlation. Option C is wrong because high correlation does not cause underfitting. Option D is wrong because heteroscedasticity is unrelated to correlation.

Answer

The model will underfit because of redundant information

Answer

Heteroscedasticity will be introduced

Answer

The model will overfit due to redundant features