MLS-C01 Exploratory Data Analysis Practice Test 5 — 15 Questions

Question 1

During exploratory data analysis, a data scientist discovers that a feature has a variance of 0.01, while other features have variances around 1.0. Which action should be taken?

Accepted Answer

Consider removing the feature or applying variance threshold.. A feature with near-zero variance (0.01) compared to others (~1.0) likely has little predictive power and can cause numerical instability in models. Variance thresholding is a standard preprocessing step to remove low-variance features. Option A is wrong: scaling to unit variance does not address the fundamental issue of low information content; it merely changes the scale. Option B is wrong: log transformation changes distribution shape but does not meaningfully increase variance; the variance will remain low. Option C is wrong: imputation is for missing values, which is unrelated to low variance.

Answer

Scale the feature to have unit variance.

Answer

Apply a log transformation to the feature.

Answer

Impute missing values in the feature.

Question 2

A data scientist wants to understand the relationship between a categorical feature with 3 levels and a continuous target variable. Which visualization is most appropriate?

Accepted Answer

Box plot grouped by category. A box plot grouped by category (Option C) is the most appropriate visualization because it directly compares the distribution of a continuous target variable across the three levels of a categorical feature. It displays median, quartiles, and potential outliers for each group, making it ideal for understanding central tendency, spread, and skewness in a side-by-side comparison.

Answer

Correlation matrix

Answer

Line chart

Answer

Scatter plot

Question 3

A data scientist is analyzing a dataset and finds that the target variable has a bimodal distribution. Which preprocessing step is most appropriate before modeling?

Accepted Answer

Consider clustering to separate the two modes and model them separately.. Bimodal distributions indicate two distinct underlying groups. Clustering can separate the modes, allowing separate models for each cluster, which often improves performance. Option A is incorrect because standardizing does not change distribution shape. Option B is incorrect because removing outliers would not address bimodality; outliers are extreme values, not necessarily related to modes. Option D is incorrect because log transformation is for skewed unimodal distributions, not bimodal.

Answer

Standardize the target variable to have mean 0 and variance 1.

Answer

Remove outliers from the target variable.

Answer

Apply a log transformation to the target variable.

Question 4

A data scientist is performing exploratory data analysis on text data. They want to identify the most common terms and their frequencies. Which approach should they use?

Accepted Answer

Create a bag-of-words matrix and compute term frequencies.. A bag-of-words matrix counts the frequency of each term in the text, directly providing the most common terms and their frequencies. Option A is incorrect because sentiment analysis determines the emotional tone of text, not term frequencies. Option B is incorrect because Latent Dirichlet Allocation (LDA) is a topic modeling technique that assigns topics to documents, not term frequencies. Option D is incorrect because word2vec generates dense vector embeddings that capture semantic relationships, not raw term frequencies.

Answer

Perform sentiment analysis on the text.

Answer

Apply Latent Dirichlet Allocation (LDA) to extract topics.

Answer

Use word2vec to generate word embeddings.

Question 5

A data scientist is analyzing a dataset with missing values in several features. The dataset is large (10 million rows) and stored in an S3 bucket as CSV files. The scientist wants to use AWS Glue to catalog the data and then use Amazon Athena to query it. However, the missing values are causing errors in downstream machine learning models. Which approach should the scientist take to handle missing values during exploratory data analysis?

Accepted Answer

Use AWS Glue ETL jobs with a custom transformation script that uses the AWS Glue library to drop or impute missing values before writing to a new dataset.. AWS Glue ETL jobs can be used with custom scripts to handle missing values by either dropping rows or imputing values using built-in transforms or custom logic. This is ideal for large-scale datasets stored in S3 as CSV files. Glue integrates with the AWS Glue library for transforming data.

Option A (SageMaker Data Wrangler) is more suitable for interactive data preparation and visualization, but not for automated, large-scale ETL processing of 10 million rows.
Option C (Redshift Spectrum) is primarily a query engine that can query data in S3, but it does not provide built-in data cleaning capabilities for missing values; you would need to use SQL functions like COALESCE, but it's not the best approach for comprehensive ETL.
Option D (Athena) is also a query engine and cannot modify the underlying data; it can impute values in query results, but not write transformed data back to S3 as a cleaned dataset without additional steps.

Answer

Use Amazon SageMaker Data Wrangler to create a data flow that imputes missing values and export the transformed dataset to S3.

Answer

Use Amazon Redshift Spectrum with an external table to query the data and use SQL COALESCE to handle missing values on the fly.

Answer

Use Amazon Athena to run SQL queries that impute missing values and write the results to a new table.

Question 6

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset includes a column 'transaction_date' with timestamps. The engineer wants to derive features such as day of the week, hour, and month for modeling. Which AWS service can be used directly to extract these features without writing custom code?

Accepted Answer

AWS Glue ETL with built-in timestamp transforms. AWS Glue ETL provides built-in transforms like `ExtractTimestamp` that can parse timestamps and extract date/time components (e.g., day of week, hour, month) without writing custom code. Option B is wrong because Amazon Athena requires writing SQL queries to extract date parts, which constitutes custom code. Option C is wrong because Amazon QuickSight is a BI visualization tool, not designed for feature engineering. Option D is wrong because Amazon SageMaker Data Wrangler, while offering visual transformations, requires an active SageMaker Studio environment and is not a serverless ETL service like AWS Glue.

Answer

Amazon Athena with SQL date functions

Answer

Amazon QuickSight

Answer

Amazon SageMaker Data Wrangler

Question 7

A data scientist is exploring a dataset with 500 features and 100,000 observations for a regression problem. The scientist notices that many features are highly correlated with each other. Which technique should the scientist use to reduce multicollinearity and improve model interpretability during exploratory data analysis?

Accepted Answer

Calculate Variance Inflation Factor (VIF) for each feature and remove those with VIF > 10.. Variance Inflation Factor (VIF) is a measure of multicollinearity among features. Removing features with high VIF (e.g., > 10) reduces multicollinearity and retains interpretability. Option A is incorrect because mutual information measures dependency between feature and target, not multicollinearity. Option B is incorrect because PCA creates new features that are linear combinations, reducing interpretability. Option C is incorrect because Lasso regression is a modeling technique, not typically used during exploratory data analysis, and it may not remove all correlated features.

Answer

Compute mutual information between each feature and the target, and keep only the top 50 features.

Answer

Apply Principal Component Analysis (PCA) to reduce the feature space.

Answer

Use Lasso regression to select features with non-zero coefficients.

Question 8

A machine learning team is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?

Accepted Answer

Bar chart of class frequencies and a correlation heatmap. A bar chart of class frequencies clearly visualizes the imbalance (99% negative vs 1% positive), and a correlation heatmap helps identify which features are correlated with the target, guiding resampling strategy. Option A is wrong because a confusion matrix is used for evaluating model predictions, not for initial exploratory data analysis of class imbalance. Option B is wrong because a scatterplot matrix is designed to visualize relationships between continuous variables and can be overwhelming with many features; it does not directly highlight the class imbalance. Option C is wrong because box plots grouped by target class show feature distributions across classes but do not explicitly quantify the imbalance ratio itself.

Answer

Confusion matrix on a sample of the data

Answer

Scatterplot matrix of all features colored by class

Answer

Box plots of each feature grouped by the target class

Question 9

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

Accepted Answer

SageMaker Data Wrangler. SageMaker Data Wrangler is a built-in visual data preparation tool in SageMaker Studio that provides summary statistics, histograms, and correlation matrices without writing code. Option A (SageMaker Ground Truth) is for data labeling, not EDA. Option C (SageMaker Autopilot) automates machine learning model building. Option D (SageMaker Clarify) is for bias detection and model explainability.

Answer

SageMaker Ground Truth

Answer

SageMaker Autopilot

Answer

SageMaker Clarify

Question 10

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. Amazon SageMaker notebooks allow custom Python scripts using libraries like scikit-learn for clustering (e.g., K-Means) and Geopy for distance calculations, making it ideal for geospatial feature engineering and clustering during EDA. Option B is incorrect: Amazon Athena with PostGIS is for querying geospatial data, not for iterative analysis or clustering. Option C is incorrect: AWS Glue is an ETL service, not suited for interactive exploration and clustering. Option D is incorrect: Amazon Location Service provides maps and location tracking APIs, not a platform for analytical clustering.

Answer

Amazon Athena with PostGIS extensions

Answer

AWS Glue with geospatial transforms

Answer

Amazon Location Service

Question 11

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. 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 12

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. Box plot. A box plot is specifically designed to show quartiles and highlight outliers as points beyond the whiskers. A line chart (A) is for trends over time, a histogram (B) shows distribution but does not isolate outliers, and a scatter plot (C) is for bivariate relationships.

Answer

Line chart

Answer

Histogram

Answer

Scatter plot

Question 13

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. (Stratified random sampling) is correct because it divides the dataset into homogeneous subgroups (strata) and samples proportionally from each, ensuring all subgroups are represented and reducing bias. This is especially important when the data is imbalanced. Option B (Simple random sampling) is incorrect because it may underrepresent or miss rare subgroups, leading to biased samples. Option C (Systematic sampling) is incorrect because it can introduce bias if the data has periodic patterns. Option D (Reservoir sampling) is incorrect because it is designed for streaming data where the total size is unknown, not for a static dataset of 10 million records in Amazon S3.

Answer

Simple random sampling

Answer

Systematic sampling

Answer

Reservoir sampling

Question 14

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. Histograms show the distribution of numerical features, helping to identify skewness and outliers. Option E is correct because summary statistics like df.describe() provide count, mean, min, max, and quartiles, which reveal missing values (via count) and outliers (via min/max). Option A is incorrect because a scatterplot matrix visualizes pairwise relationships but does not directly show missing values or outliers. Option C is incorrect because a confusion matrix is used for evaluating classification model performance, not for data exploration. Option D is incorrect because a correlation matrix shows relationships between features but does not highlight missing values or outliers.

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 15

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). In a high-dimensional dataset (p >> n), feature selection is crucial. Option B (Variance Threshold) is suitable because features with low variance (e.g., <0.01) are likely to be constant or near-constant and thus uninformative. Option C (removing features with high pairwise correlation >0.95) helps reduce redundancy and multicollinearity. Option D (mutual information) is a filter method that measures dependency between each feature and the target, allowing selection of the most relevant features. Option A (Lasso regression) is a modeling method that can be used for feature selection but is not typically used during EDA; it is a supervised learning technique. Option E (PCA) is a dimensionality reduction technique that creates new components, not feature selection. Therefore, the correct answers are B, C, D.

Answer

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

Answer

Apply Principal Component Analysis (PCA) and select top components