1. Question 1

What does a typical data wrangling workflow include?

• ❌ Using mathematical techniques to identify correlations in data

• ❌ Predicting probabilities

• ❌ Recognizing patterns

• ✅ Validating the quality of the transformed data

Explanation:
Data wrangling involves cleaning, transforming, and validating data quality before analysis.

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2. Question 2

OpenRefine is an open-source tool that allows you to:

• ✅ Transform data into formats like TSV, CSV, XLS, XML, JSON

• ❌ Enforce data governance policies

• ❌ Automatically detect schemas and anomalies

• ❌ Use Microsoft Power Query add-ins

Explanation:
OpenRefine is mainly used for cleaning and transforming data across many formats.

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3. Question 3

What is one step in a typical data cleaning workflow?

• ❌ Building classification models

• ✅ Inspecting data to detect issues and errors

• ❌ Clustering data

• ❌ Establishing relationships between data events

Explanation:
Data cleaning always starts with examining data to find errors, missing values, and inconsistencies.

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4. Question 4

When you combine rows from multiple source tables into one table, what transformation is this?

• ❌ Joins

• ❌ Denormalization

• ❌ Normalization

• ✅ Unions

Explanation:
A UNION stacks rows from multiple tables with similar structure into a single combined result set.

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5. Question 5

A value vastly different from others in a dataset is called:

• ❌ Missing value

• ❌ Irrelevant data

• ❌ Syntax error

• ✅ Outlier

Explanation:
An outlier is a value that stands far outside the normal range of the dataset.

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🧾 Summary Table

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