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

Question 1

A data engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 (10 TB in CSV format). The dataset has 2000 columns and 50 million rows. The engineer needs to compute summary statistics (mean, median, standard deviation) for each numeric column and identify missing values. Which approach is MOST cost-effective and time-efficient?

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

Accepted Answer

Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.. Converting the CSV data to columnar Parquet and querying with Athena is the most cost-effective and time-efficient approach. Parquet is columnar and compressed, so Athena scans only the columns needed for statistics, drastically reducing data scanned and cost. Athena is serverless, so there is no cluster to provision, and it can compute aggregates directly over S3.

Answer

Use Amazon Redshift Spectrum to query the data directly from S3.

Answer

Load the data into Amazon SageMaker Data Wrangler and compute statistics interactively.

Answer

Use AWS Glue ETL to compute statistics and write results to S3.

Question 2

A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying to the target variable to improve model performance?

Accepted Answer

Apply log transformation to the target variable.. Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.

Answer

Apply StandardScaler to the target variable.

Answer

Apply MinMaxScaler to the target variable.

Answer

Apply one-hot encoding to the target variable.

Question 3

A data scientist is working with a dataset containing customer transactions. The dataset has a column named 'transaction_date' with timestamp values. The scientist wants to create new features such as day of week, hour, and whether the transaction occurred on a weekend. Which AWS service provides built-in feature engineering capabilities for datetime columns?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler includes built-in transformations for datetime features like extracting day, month, hour, etc. Option B (Amazon Athena) can extract parts but not as a feature engineering step. Option C (AWS Glue ETL) requires custom code. Option D (Amazon EMR) requires more manual effort.

Answer

Amazon Athena

Answer

AWS Glue ETL

Answer

Amazon EMR

Question 4

During exploratory data analysis, a data scientist notices that a categorical feature 'city' has over 1,000 unique values. The dataset has 10,000 rows. Which technique should the scientist consider to reduce the cardinality of this feature?

Accepted Answer

Group low-frequency categories into a single 'other' category.. Grouping rare categories into an 'other' bucket is a common technique to reduce cardinality. Option A (label encoding) assigns numeric labels but still has 1000 unique values. Option B (grouping into 'other') reduces cardinality. Option C (one-hot encoding) would create too many columns. Option D (frequency encoding) replaces categories with frequency but still has 1000 values.

Answer

Apply label encoding to assign numeric labels.

Answer

Apply one-hot encoding to all categories.

Answer

Apply frequency encoding to replace each category with its frequency.

Question 5

A data scientist is performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?

Accepted Answer

Calculate mutual information between each feature and the target.. Mutual information (MI) measures the dependency between two variables and captures both linear and nonlinear relationships, making it ideal for detecting strong nonlinear associations between features and a target. Unlike correlation, MI does not assume a linear form and works with mixed data types when discretized appropriately. This makes it the most appropriate technique for the stated goal.

Answer

Use ANOVA to compare feature means across target classes.

Answer

Compute Pearson correlation coefficients.

Answer

Perform chi-squared tests for each feature.

Question 6

A data engineer is running an Amazon SageMaker Data Wrangler flow on a dataset with 5 million rows. The flow includes several transformations. The engineer wants to validate the data quality by checking for missing values and outliers before training. Which approach is most efficient?

Accepted Answer

Use Data Wrangler's data quality and insights report to generate a report with statistics and visualizations.. SageMaker Data Wrangler includes a built-in Data Quality and Insights Report that automatically analyzes datasets for missing values, outliers, duplicate rows, and target leakage, producing statistics and visualizations. For a 5-million-row dataset already in a Data Wrangler flow, this is the most efficient native approach because it runs within the same tool without exporting data or spinning up additional compute.

Answer

Export the transformed data to S3 and query with Amazon Athena.

Answer

Use Amazon EMR with Spark to compute statistics.

Answer

Import the data into Amazon QuickSight and create dashboards.

Question 7

Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Remove rows or columns with missing values if they are few.. Option B is correct because listwise deletion (dropping rows) or dropping entire columns is a standard, simple technique when the proportion of missing data is small, minimizing information loss and bias. Option E is correct because mean or median imputation replaces missing entries with a central tendency statistic of the column, preserving sample size and being especially suitable for numerical features (median being robust to outliers). Option A is not a missing-value technique; feature scaling (e.g., Min-Max or StandardScaler) only rescales existing values and cannot handle NaNs. Option C, PCA, is a dimensionality-reduction method and does not address missing values directly (it typically requires complete data or prior imputation). Option D is incorrect because one-hot encoding converts categorical variables into binary vectors and does not fill or handle missing values.

Answer

Apply feature scaling to normalize the data.

Answer

Use Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Apply one-hot encoding to the missing values.

Question 8

Which THREE of the following are valid techniques for detecting outliers in a dataset during exploratory data analysis? (Select THREE.)

Accepted Answer

Z-score method: flag points with absolute Z-score > 3.. The Z-score method (A) is a valid univariate outlier detection technique: it standardizes each value as z = (x − μ)/σ and flags observations whose absolute Z-score exceeds 3, i.e., points more than three standard deviations from the mean, which are statistically rare under an approximately normal distribution. The Isolation Forest algorithm (C) is a valid model-based anomaly detection method that builds an ensemble of random trees and scores points by how few random splits are needed to isolate them, so outliers (which isolate quickly with short path lengths) receive high anomaly scores. The IQR method (E) is a valid non-parametric technique that computes Q1 and Q3, defines the interquartile range IQR = Q3 − Q1, and flags points below Q1 − 1.5*IQR or above Q3 + 1.5*IQR as outliers, making it robust to non-normal data and extreme values. Linear regression residuals (B) are used to assess model fit and heteroscedasticity rather than as a standalone outlier-detection technique, and K-means clustering (D) is an unsupervised partitioning algorithm that assigns points to clusters but does not itself define an outlier criterion, so neither belongs here.

Answer

Linear regression residuals.

Answer

K-means clustering.

Question 9

Which TWO of the following are best practices for exploratory data analysis when using Amazon SageMaker Data Wrangler? (Select TWO.)

Accepted Answer

Use Data Wrangler's built-in data visualizations to explore feature distributions and relationships.. Option B is correct because SageMaker Data Wrangler includes built-in visualization tools (histograms, scatter plots, box plots, and correlation matrices) that let you quickly inspect feature distributions, detect outliers, and understand relationships between variables during exploratory data analysis without leaving the tool. Option E is correct because Data Wrangler lets you export your data flow as a Jupyter notebook, which packages the transformation steps as reproducible code that teammates can review, modify, and rerun, making it a recommended practice for collaboration and reproducibility. Option A is not a best practice because Athena is a query service over S3/Glue catalog data, not a store for intermediate EDA results, and Data Wrangler already provides in-memory profiling and visualization. Option C is unnecessary since Data Wrangler performs data profiling natively without requiring you to spin up Amazon EMR Spark jobs. Option D is wrong because exporting to QuickSight before transformation is inefficient; Data Wrangler's built-in analysis and transformation capabilities should be used first, and QuickSight is a BI visualization service, not an EDA prerequisite.

Answer

Store all intermediate results in Amazon Athena for querying.

Answer

Use Amazon EMR to run Spark jobs for data profiling.

Answer

Always export the data to Amazon QuickSight for analysis before transformation.

Question 10

A data scientist needs to understand the distribution of a numeric feature in a dataset stored in Amazon S3. Which AWS service can be used to run a quick exploratory query without setting up a server?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless, interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. It requires no infrastructure setup or management, making it ideal for quick exploratory queries on data stored in S3. You simply point Athena to your S3 data, define a table in the AWS Glue Data Catalog, and run SQL queries to understand the distribution of a numeric feature. This aligns perfectly with the requirement to avoid server setup.

Answer

Amazon Redshift

Answer

Amazon EMR

Answer

AWS Glue

Question 11

A data scientist is analyzing a dataset with a timestamp column. The goal is to identify seasonality and trends. Which visualization technique is most suitable?

Accepted Answer

Time series line plot of the target variable over time.. A time series line plot is the standard visualization for identifying trends and seasonality over time. Option B (box plot grouped by day of week) can show distributions but may not reveal trends or seasonality clearly. Option C (scatter plot of target vs. timestamp) can show patterns but may be less clear than a line plot for time series. Option D (heatmap of correlations) is for exploring relationships between features, not for time series analysis.

Answer

Box plot of the target variable grouped by day of week.

Answer

Scatter plot of the target variable vs. the timestamp.

Answer

Heatmap of correlation between all features.

Question 12

A company uses Amazon SageMaker Data Wrangler to perform exploratory data analysis. They want to detect outliers in a numerical column using the Interquartile Range (IQR) method. Which transformation should they apply in Data Wrangler?

Accepted Answer

Handle outliers. In Amazon SageMaker Data Wrangler, the 'Handle outliers' transformation is specifically designed to detect and treat outliers, and it supports the Interquartile Range (IQR) method as one of its detection strategies. Applying this transformation allows the user to identify outliers in a numerical column using IQR and choose how to handle them (e.g., remove or cap).

Answer

Impute

Answer

Normalize

Answer

Binning

Question 13

A data scientist is exploring a dataset with many missing values. They want to understand the pattern of missingness before deciding on imputation. Which approach is most appropriate?

Accepted Answer

Visualize the missingness using a heatmap or bar chart.. Visualizing missingness with a heatmap (e.g., missingno.matrix) or bar chart (missingno.bar) reveals patterns such as whether missing values are random, clustered by row/column, or correlated across features. This diagnostic step informs the choice of imputation strategy — for example, MCAR vs. MAR vs. MNAR — before any values are filled or dropped. It is the standard first step in missing-data exploration.

Answer

Compute the correlation matrix of the features with missing values.

Answer

Drop all rows with any missing values.

Answer

Impute all missing values with the mean of each column.

Question 14

Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)

Accepted Answer

Impute with the mode (most frequent) of the column.. Option B is correct because imputing missing categorical values with the mode (most frequent category) preserves the column's most common label and keeps the dataset size intact, which is a standard and appropriate EDA technique for categorical variables. Option C is correct because treating missing values as a distinct 'Unknown' category retains the information that the value was missing and lets the analysis capture any pattern associated with missingness, which is often meaningful in categorical data. Option A is not appropriate here because one-hot encoding is a representation technique for existing categories, not a missing-data handling strategy, and encoding missingness as a binary feature alone does not resolve the missing categorical value. Option D is not appropriate because dropping all rows with missing values discards potentially large amounts of data and can introduce bias, which is generally undesirable during EDA. Option E is not appropriate because the mean is only defined for numeric data and is meaningless for categorical variables.

Answer

Use one-hot encoding to represent missingness as a binary feature.

Answer

Drop all rows with missing values in that column.

Answer

Impute missing values with the mean of the column.

Question 15

Which THREE actions are valid steps in exploratory data analysis when working with a new dataset? (Choose three.)

Accepted Answer

Check the data types of each column.. Option A is correct because verifying each column's data type (e.g., int64, float64, object, datetime) is a foundational EDA step that reveals whether values are encoded properly and whether type conversions are needed before analysis. Option B is correct because descriptive statistics such as mean, standard deviation, min, and max (typically produced via pandas .describe() or similar) summarize central tendency, spread, and range, exposing outliers, skew, and scale issues in the new data. Option E is correct because histograms visualize the distribution of numerical features, making skewness, multimodality, and gaps immediately apparent, which guides later preprocessing and modeling decisions. Option C does not belong because fitting a linear regression model is a predictive modeling task, not an EDA step, and it presumes target and feature roles that EDA is meant to inform. Option D does not belong because splitting into training and test sets is a model-validation preparation step, not part of exploring and understanding a new dataset's structure and distributions.

Answer

Fit a linear regression model to identify important features.

Answer

Split the dataset into training and test sets.