MLS-C01 Exploratory Data Analysis Practice Test 13 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?

Accepted Answer

Create a correlation matrix and visualize with a heatmap. A correlation matrix with a heatmap visualizes pairwise correlations and helps identify groups of correlated features. Option B is wrong because PCA reduces dimensionality but does not show feature groups directly. Option C is wrong because mutual information measures dependency but not specifically linear correlation. Option D is wrong because chi-square test is for categorical associations. Option E is wrong because t-SNE is for visualization of high-dimensional data, not for correlation analysis.

Answer

Use t-SNE to visualize feature relationships

Answer

Apply PCA and examine the loadings

Answer

Compute mutual information between each feature and the target

Answer

Use chi-square test for each pair

Question 2

A data scientist is exploring a large dataset (10 TB) stored in Amazon S3. The dataset is in CSV format and has many columns. The scientist wants to quickly compute summary statistics (mean, min, max, count) for each column without moving the data. Which approach is most cost-effective and efficient?

Accepted Answer

Use Amazon Athena with SQL queries. Using Amazon Athena with SQL queries allows serverless querying of data in S3, and is cost-effective (pay per query). It can compute summary statistics directly on the data without moving it. Option A is wrong because SageMaker Data Wrangler requires importing data into SageMaker, which may incur transfer costs and time. Option B is wrong because Glue DataBrew also processes data but may be more expensive for large datasets. Option D is wrong because S3 Select works on single objects and is limited. Option E is wrong because launching an EMR cluster adds overhead and cost for a simple task.

Answer

Import the data into Amazon SageMaker Data Wrangler

Answer

Launch an Amazon EMR cluster with Spark

Answer

Use S3 Select to compute statistics

Answer

Use AWS Glue DataBrew to profile the data

Question 3

A data scientist is performing EDA on a dataset with mixed data types (numerical and categorical). Which TWO visualizations are most appropriate for understanding the distribution of categorical features?

Accepted Answer

Pie chart. A bar chart shows the count of each category, and a pie chart shows the proportion. Both are suitable for categorical data. Options C, D, and E are for numerical data.

Answer

Histogram

Answer

Box plot

Answer

Scatter plot

Question 4

A data scientist is analyzing a dataset and suspects the presence of outliers that could affect the mean and standard deviation. Which TWO methods are robust to outliers for measuring central tendency and dispersion?

Accepted Answer

Interquartile range (IQR). Median and interquartile range (IQR) are robust to outliers. Mean and standard deviation are sensitive to outliers. Range is also sensitive.

Answer

Range

Answer

Standard deviation

Answer

Mean

Question 5

A data scientist is performing EDA on a dataset with a binary target variable. Which THREE techniques can help assess the relationship between a continuous feature and the target?

Accepted Answer

KDE plot grouped by target. Box plots (comparing distributions for each class), histograms (overlay or side-by-side), and KDE plots (probability density) are all effective for visualizing the relationship between a continuous feature and a binary target. Option D (scatter plot) requires two continuous variables. Option E (bar chart) is for categorical features.

Answer

Scatter plot against another continuous feature

Answer

Bar chart of feature values

Question 6

A data scientist is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?

Accepted Answer

Use AWS Glue DataBrew to create a profile job that outputs data quality reports. AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option B is wrong because SageMaker Data Wrangler requires more manual setup and coding. Option C is wrong because Athena requires SQL queries and is not a profiling tool. Option D is wrong because Glue ETL jobs require writing code.

Answer

Use Amazon SageMaker Data Wrangler to import the data and generate a report

Answer

Use Amazon Athena to run SELECT statements on each column

Answer

Use AWS Glue ETL jobs with PySpark to compute statistics

Question 7

A data scientist is analyzing a dataset with a large number of missing values in several columns. The dataset is stored in an Amazon S3 bucket and is about 5 TB in size. The scientist wants to understand the pattern of missingness (e.g., is it missing completely at random, missing at random, or not missing at random) before deciding on an imputation strategy. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Studio. Which approach should the scientist take to best understand the missing data patterns?

Accepted Answer

Use AWS Glue DataBrew's data quality and missing data reports. AWS Glue DataBrew provides a missing data report that includes patterns and correlations of missingness, such as heatmaps and bar charts. This helps determine the type of missingness. Option B is wrong because SageMaker Data Wrangler does not have built-in missingness pattern analysis. Option C is wrong because Athena is for querying, not pattern analysis. Option D is wrong because Glue ETL jobs require custom coding and are less efficient for exploratory analysis.

Answer

Use Amazon SageMaker Data Wrangler to create a flow and analyze missingness visually

Answer

Use AWS Glue ETL jobs with PySpark to compute missingness statistics

Answer

Use Amazon Athena to run queries to find missing values per column

Question 8

A data scientist is performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?

Accepted Answer

Define reasonable bounds based on domain knowledge and filter or cap the outliers. Using domain knowledge to define valid age range (e.g., 0-120) and filtering out or capping outliers is the most appropriate approach. Option B is wrong because removing the entire column loses information. Option C is wrong because imputing with mean distorts the distribution if outliers are extreme. Option D is wrong because standard scaling does not handle outliers; it will still be affected.

Answer

Apply standard scaling to the 'age' column

Answer

Impute the outlier values with the mean of the column

Answer

Remove the 'age' column entirely

Question 9

A data scientist is analyzing a dataset with missing values in several columns. The dataset is stored in an S3 bucket. What is the most efficient method to identify the percentage of missing values per column using AWS services?

Accepted Answer

Use Amazon Athena to query the data with SQL using COUNT(*) and CASE statements to compute missing percentage per column.. Option C is correct because Amazon Athena allows running SQL queries directly on data in S3, and the COUNT and CASE statements can compute missing value percentages efficiently without moving data. Option A is wrong because AWS Glue Crawler only catalogs metadata, not performing data analysis. Option B is wrong because SageMaker Notebook requires manual coding and is less efficient for quick checks. Option D is wrong because QuickSight is a visualization tool, not for direct SQL-based analysis.

Answer

Use Amazon SageMaker Notebook with pandas to load the dataset and compute missing percentages.

Answer

Use Amazon QuickSight to connect to S3 and calculate missing value percentages via calculated fields.

Answer

Use AWS Glue Crawler to infer schema and view missing values statistics in the AWS Glue Data Catalog.

Question 10

A machine learning engineer is performing exploratory data analysis on a large dataset stored in S3 using Amazon Athena. The dataset contains a timestamp column 'event_time' of type string. The engineer wants to analyze daily trends. Which approach is the most cost-effective and efficient?

Accepted Answer

Partition the table by date derived from the event_time string and query using partition filtering.. Option D is correct because converting the string to a date type in the query allows Athena to use partition pruning if the table is partitioned by date, reducing scanned data. Option A is wrong because CAST in SELECT still scans all data. Option B is wrong because creating a view does not reduce data scanned. Option C is wrong because converting to Parquet is beneficial but not the most direct for the given task.

Answer

Create a view that casts the column to timestamp and query the view.

Answer

Use the CAST function in the SELECT statement to convert the string to timestamp.

Answer

Convert the data to Parquet format with a timestamp column and re-query.

Question 11

A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?

Accepted Answer

Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.. Option A is correct because correlation-based feature selection removes redundant features, and target encoding handles high-cardinality categoricals without expanding dimensions. Option B is wrong because PCA reduces interpretability and does not handle categoricals. Option C is wrong because chi-squared test is for categorical targets, but dataset has binary target; also one-hot encoding explodes dimensions. Option D is wrong because mutual information is used for feature selection but does not address high cardinality directly.

Answer

Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.

Answer

Use mutual information to select top features and apply label encoding to categorical variables.

Answer

Use chi-squared test to select top features and one-hot encode categorical variables.

Question 12

An organization stores streaming data in Amazon Kinesis Data Streams. A data analyst wants to perform real-time exploratory data analysis on the incoming data to detect anomalies. Which AWS service should the analyst use to run SQL queries on the streaming data?

Accepted Answer

Amazon Kinesis Data Analytics. Option B is correct because Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time. Option A is wrong because Athena is for batch queries on S3. Option C is wrong because Glue is for ETL, not real-time SQL. Option D is wrong because SageMaker is for ML model training, not streaming SQL.

Answer

Amazon SageMaker

Answer

AWS Glue

Answer

Amazon Athena

Question 13

A data scientist is using Amazon SageMaker Data Wrangler for exploratory data analysis. The dataset contains a column with missing values that are encoded as 'NA' strings. The data scientist wants to treat these as missing values during the import. Which step should the data scientist take?

Accepted Answer

Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.. Option C is correct because Data Wrangler allows specifying custom missing value symbols during import. Option A is wrong because dropping rows is premature. Option B is wrong because replacing after import is less efficient. Option D is wrong because imputation is needed after treating missing values.

Answer

Use the 'Impute' transform to fill 'NA' with the mean of the column.

Answer

Use the 'Replace missing' transform to replace 'NA' with null after import.

Answer

Use the 'Drop missing' transform to remove rows containing 'NA'.

Question 14

A data engineer is performing EDA on a dataset with 1 million rows and 200 columns. The dataset is stored in S3 as CSV files. The engineer notices that some columns have a high proportion of zeros. What is the best approach to determine if these zeros represent missing data or actual zero values?

Accepted Answer

Consult the data source documentation or domain experts to understand the meaning of zero values.. Option D is correct because domain knowledge and documentation are the most reliable ways to understand the meaning of zeros. Option A is wrong because statistical methods cannot distinguish missing vs actual zero without context. Option B is wrong because metadata may not have this detail. Option C is wrong because comparing to other columns might be misleading.

Answer

Check correlation of zero columns with other features; if low, assume zeros are missing.

Answer

Calculate the percentage of zeros and compare with other columns; if unusually high, treat as missing.

Answer

Use AWS Glue Data Catalog to view column statistics and infer missing values.

Question 15

A data scientist is analyzing a dataset with a skewed target variable for a regression problem. During EDA, the scientist wants to transform the target variable to approximate a normal distribution. Which transformation should the scientist apply first?

Accepted Answer

Box-Cox transformation. Option B is correct because Box-Cox is designed to make data more normally distributed and handles positive values. Option A is wrong because log is a special case of Box-Cox but less general. Option C is wrong because scaling does not change distribution shape. Option D is wrong because quantile transformation can be used but may overfit; Box-Cox is parametric and often preferred.

Answer

Quantile transformation

Answer

Min-Max scaling

Answer

Log transformation