MLS-C01 Exploratory Data Analysis Practice Test 19 — 15 Questions

Question 1

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 (one-hot encoding) would create too many columns. Option B (label encoding) doesn't reduce uniqueness. 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 2

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 can capture any kind of dependency (including nonlinear) between features and target. Option A (Pearson correlation) only linear. Option B (Chi-squared test) is for categorical features. Option D (ANOVA) is for comparing means across groups.

Answer

Use ANOVA to compare feature means across target classes.

Answer

Compute Pearson correlation coefficients.

Answer

Perform chi-squared tests for each feature.

Question 3

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.. Using Data Wrangler's built-in data quality and insights report is the most efficient way to get statistics and detect issues without custom code. Option B (Athena) requires writing SQL queries. Option C (QuickSight) needs exporting. Option D (EMR) is overkill.

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 4

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.. Imputation with mean/median and removing rows/columns are standard techniques. Options C (one-hot encoding), D (PCA), and E (feature scaling) are not for handling 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 5

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.. Z-score, IQR, and Isolation Forest are all common outlier detection methods. Option D (Linear regression) is not for outlier detection. Option E (K-means) can be used for clustering but not primarily for outlier detection.

Answer

Linear regression residuals.

Answer

K-means clustering.

Question 6

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.. Using Data Wrangler's built-in visualizations for quick analysis and exporting the flow as a Jupyter notebook for reproducibility are best practices. Option B (QuickSight) is separate. Option C (EMR) is not needed. Option D (Athena) is for queries, not for building into pipeline.

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 7

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. Option C is correct because Amazon Athena allows serverless SQL queries on data in S3. Option A (Amazon EMR) requires cluster setup; Option B (AWS Glue) is for ETL; Option D (Amazon Redshift) is a data warehouse.

Answer

Amazon Redshift

Answer

Amazon EMR

Answer

AWS Glue

Question 8

A data scientist is analyzing a dataset with 10 million rows and 50 columns. The target variable is highly imbalanced (99% negative, 1% positive). Which approach is most appropriate for exploratory data analysis before modeling?

Accepted Answer

Take a stratified sample that preserves the 99:1 ratio.. Option B is correct because stratified sampling preserves the class proportion in the sample, which is critical for imbalanced data. Option A (random sample) may miss positives; Option C (up-sample minority) changes distribution; Option D (remove negatives) loses information.

Answer

Remove all negative examples and analyze only the positive ones.

Answer

Take a random sample of 100,000 rows from the entire dataset.

Answer

Up-sample the minority class to balance the dataset before analysis.

Question 9

During exploratory data analysis, a data scientist notices that the correlation matrix of features shows many pairs with absolute correlation > 0.95. The dataset includes both numerical and categorical variables. Which technique is most appropriate to reduce multicollinearity while preserving the most information?

Accepted Answer

Apply Principal Component Analysis (PCA) to the features.. Option D is correct because PCA is a dimensionality reduction technique that handles multicollinearity by creating orthogonal components. Option A (remove one from each pair) is ad-hoc; Option B (regularization) is for modeling, not EDA; Option C (use only one-hot encoded features) loses information.

Answer

Use only one-hot encoded categorical features.

Answer

Apply L1 regularization during model training.

Answer

Remove one feature from each highly correlated pair.

Question 10

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.. Option B is correct because a time series line plot is standard for visualizing trends and seasonality over time. Option A (scatter plot) is for two numerical variables; Option C (heatmap) shows correlation; Option D (box plot) shows distribution.

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 11

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. Option A is correct because Data Wrangler has a built-in 'Handle outliers' transform that allows IQR-based detection. Option B (Normalize) scales data; Option C (Binning) groups values; Option D (Impute) fills missing values.

Answer

Impute

Answer

Normalize

Answer

Binning

Question 12

A data scientist is performing EDA on a dataset with 1 million rows. They suspect the dataset contains duplicate rows. Which approach is most efficient to identify duplicates in Amazon SageMaker Studio?

Accepted Answer

Use DuckDB SQL query: SELECT COUNT(*) - COUNT(DISTINCT *) FROM table.. Option C is correct because DuckDB is an in-process SQL OLAP database that can run on a single machine and efficiently handle large datasets. Option A (Python loop) is slow; Option B (pandas drop_duplicates) may be memory-intensive; Option D (Athena) is serverless but incurs cost and latency.

Answer

Write a Python script that loops through each row and compares to a set of seen rows.

Answer

Use pandas drop_duplicates and then check the length difference.

Answer

Use Amazon Athena to query the S3 data with COUNT(DISTINCT *).

Question 13

Which AWS service can be used to generate a data profile (including histograms, correlations, and statistics) for a dataset stored in Amazon S3 without writing code?

Accepted Answer

Amazon SageMaker Data Wrangler. Option D is correct because Amazon SageMaker Data Wrangler provides a visual interface to create data profiles. Option A (QuickSight) is for visualization, not profiling; Option B (Glue DataBrew) also profiles but Data Wrangler is more integrated with SageMaker; Option C (Athena) is for querying.

Answer

Amazon QuickSight

Answer

AWS Glue DataBrew

Answer

Amazon Athena

Question 14

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.. Option A is correct because a heatmap of missing values (using libraries like missingno) visually shows patterns. Option B (drop rows) is premature; Option C (mean imputation) assumes MCAR; Option D (correlation matrix) does not show missingness patterns.

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 15

A data scientist is using Amazon SageMaker Studio notebooks for EDA. They want to share a reproducible report that includes code, visualizations, and narrative text with their team. Which approach should they use?

Accepted Answer

Save the notebook as an .ipynb file and share it via Amazon S3.. Option C is correct because a Jupyter notebook (in Studio) contains code and markdown and can be shared. Option A (QuickSight dashboard) is for interactive dashboards; Option B (SageMaker Autopilot) is for automated ML; Option D (SageMaker Clarify) is for bias detection.

Answer

Use Amazon SageMaker Clarify to generate an EDA report.

Answer

Export the results to Amazon QuickSight and create a dashboard.

Answer

Use Amazon SageMaker Autopilot to generate a report.