Module 4 challenge :Process Data from Dirty to Clean (Google Data Analytics Professional Certificate) Answers 2025

Question 1

Fill in the blank: A data scientist keeps code for data analysis pipelines in a _____, which enables them to track the evolution of the pipelines over time.

✅ Version control system
❌ Changelog
❌ Dashboard
❌ Dataset

Explanation:
A version control system (VCS) like Git helps data scientists track, compare, and manage changes to code or pipelines, enabling collaboration and rollback if needed.

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Question 2

Large number of respondents mention website loading times (not related to mobile app usability). What should you do first?

✅ Pause and reassess whether focusing on the desktop loading time comments aligns with the original project goal centered on the mobile app checkout usability.
❌ Double-check data cleaning
❌ Start separate analysis immediately
❌ Remove all unrelated responses

Explanation:
Verification includes checking relevance to the original project goal. Taking a big-picture view ensures focus remains on intended objectives before acting on unexpected findings.

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Question 3

Which SQL clause will return “island” when the condition ‘Barbados’ is met?

✅ CASE WHEN country = ‘Barbados’ THEN ‘island’ END
❌ CASE country = ‘Barbados’ THEN ‘island’ END
❌ WHEN country = ‘condition’ CASE ‘island’ END
❌ WHEN CASE country = ‘Barbados’ THEN ‘island’ END

Explanation:
Correct CASE syntax in SQL:

This returns “island” for Barbados rows.

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Question 4

Recording data cleaning efforts to recover errors and confirm data quality:

✅ Documentation
❌ Examination
❌ Illumination
❌ Disclosure

Explanation:
Documentation means recording each step of cleaning or transformation for transparency, reproducibility, and accountability in data workflows.

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Question 5

You start a complex SQL project that will take over a year — how to document query changes?

✅ Write a changelog
❌ Open a notepad
❌ Create a spreadsheet
❌ Visualize data

Explanation:
A changelog records all updates, fixes, and modifications to SQL scripts — essential for tracking progress in long-term projects.

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Question 6

A junior data analyst wants to count how many times a product ID error occurs in Google Sheets.

✅ COUNTA
❌ CONCAT
❌ CHECK
❌ CASE

Explanation:
COUNTA() counts all non-empty cells — used to count occurrences of any text or number values in a dataset (like repeated product IDs).

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Question 7

Positive outcomes of reporting on data cleaning and acting on feedback:

✅ It can uncover systemic issues and inefficiencies.
✅ It builds stakeholder confidence in the data’s reliability.
✅ It helps identify error patterns and improve data collection methods.
❌ It clears you and your team of blame for errors.

Explanation:
Transparent reporting encourages trust, continuous improvement, and process optimization — not blame-shifting.

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Question 8

To change every instance of “Green Thumb Inc.” to “Farmer’s Friend”:

✅ Find and replace
❌ Formatting
❌ Remove duplicates
❌ TRIM

Explanation:
Find and replace automates text replacement in spreadsheets — perfect for rebranding, correcting typos, or updating values in bulk.

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🧾 Summary Table

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