Module 4: Organize and Protect Data Answers (Part 2: Q16–33)

This is Part 2 of the Module 1 quiz answers for “Introducing Data Analytics and Analytical Thinking” from the Google Data Analytics Professional Certificate on Coursera.

Here, we’ll walk through questions 16 to 33 with detailed explanations to support your learning.

To find answers to the remaining questions, check out the full module breakdown below:

  • Part 1 (Q1–15)
  • Part 2 (Q16–33)

16. To reduce clutter, a data analyst hides cells that contain long, complex formulas. To view the formulas again, the analyst will need to adjust the spreadsheet sharing or encryption settings.

  • True
  • False ✅

Explanation:
Hiding and un-hiding cells is a basic spreadsheet function. It does not involve encryption or sharing permissions.

17. A data analyst is working with a file from a customer satisfaction survey. The survey was sent to anyone who became a customer between April and June, 2020. Which of the following is an effective name for the file?

  • April_May_June_2020_Responses_to_New_Customer_Survey_ANALYSISDATA_928310
  • NewCustomerSurvey_2020-6-20_V03 ✅
  • Survey_Responses
  • Apr-June2020_CustSurvey_V

Explanation:
The file name NewCustomerSurvey_2020-6-20_V03 is effective because it is concise, descriptive, includes the project name, date, and version, and is easily understood.

18. Foldering may be used by data analysts to organize folders into what?

  • Databases
  • Subfolders ✅
  • Versions
  • Tables

Explanation:
Foldering refers to organizing data into subfolders to improve structure and accessibility.

19. Data analysts use archiving to separate current from past work. It also cuts down on clutter.

  • True ✅
  • False

Explanation:
Archiving is the process of moving completed project files to a separate location, helping reduce clutter and separating current work from past work.

20. Fill in the blank: Data analysts create _____ to structure their folders.

  • scales
  • sequences
  • ladders
  • hierarchies ✅

Explanation:
Folder hierarchies provide a structured way to organize information, making it easier to locate files within nested folders.

21. A data analyst wants to ensure only people on their analytics team can access, edit, and download a spreadsheet. They can use which of the following tools? Select all that apply.

  • Sharing permissions ✅
  • Templates
  • Filtering
  • Encryption ✅

Explanation:
Sharing permissions control access to a file, while encryption secures data by preventing unauthorized access.

22. A data analyst wants to share spreadsheet tab A with their team. They’re still working with tabs B and C, and they don’t want their team members to access them yet. Hiding tabs B and C will protect them from being accessed.

  • True
  • False ✅

Explanation:
Simply hiding tabs does not secure them from access. The tabs can still be unhidden or accessed by someone with the appropriate permissions. More secure methods, such as password protection or access control, are required for data protection.

23. A data analytics team labels its files to indicate their content, creation date, and version number. The team is using what data organization tool?

  • File-naming verifications
  • File-naming conventions ✅
  • File-naming attributes
  • File-naming references

Explanation:
File-naming conventions help teams stay consistent and organized, making it easy to find, track, and manage files.

24. To align file naming and storage practices, it’s useful to develop metadata practices with your data analytics team.

  • True ✅
  • False

Explanation:
Developing metadata practices ensures that the team is consistent in how they describe, store, and retrieve data. Metadata provides context and makes files easier to manage and locate.

25. What process do data analysts use to keep project-related files together and organize them into subfolders?

  • Foldering ✅
  • Encrypting
  • Editing
  • Naming

Explanation:
Foldering involves creating a structured folder system to keep related files together. It improves organization and efficiency when managing large volumes of data.

26. A data analyst completes a project. They move project files to another location to keep them separate from their current work. This is an example of what process?

  • Renaming files
  • Archiving files ✅
  • Destroying files
  • Duplicating files

Explanation:
Archiving is the process of moving completed work to another location so it doesn’t clutter current project space but is still accessible if needed.

27. A data analyst adds sharing permissions to limit who can edit the data contained within a file. This is an example of what?

  • Data validation
  • Data integrity
  • Data security ✅
  • Data ethics

Explanation:
Sharing permissions are a part of data security, ensuring that only authorized users can access or modify sensitive information.

28. What aspects of a file do file-naming conventions typically describe? Select all that apply.

  • Creation date ✅
  • Content ✅
  • Version number ✅
  • Collaborators

Explanation:
File-naming conventions usually include the content, creation date, and version to make files easier to recognize and organize. Collaborators are typically managed through metadata or permissions, not file names.

29. Fill in the blank: A data analytics team uses _____ to indicate consistent naming conventions for a project. This is an example of using data about data.

  • folder hierarchies
  • classifications
  • metadata ✅
  • version control

Explanation:
Metadata is data about data. It includes details like file naming rules, content description, and version history, helping teams stay organized and consistent.

30. A data analyst creates a file that lists people who donated to their organization’s fund drive. An effective name for the file is FundDriveDonors_20210216_V01.

  • True ✅
  • False

Explanation:
This file name follows proper naming conventions: it includes project name, date, and version number, making it easy to identify and track.

31. Data analysts use archiving to separate current from past work. What does this process involve?

  • Using secure data-erase software to destroy old files
  • Reviewing current data files to confirm they’ve been cleaned
  • Moving files from completed projects to another location ✅
  • Reorganizing and renaming current files

Explanation:
Archiving involves relocating completed work to keep active workspaces clean and focused, while still maintaining access to past work if needed.

32. Data analysts create hierarchies to organize their folders. They do this by structuring folders by specific topics at the top, then more broadly below.

  • True
  • False ✅

Explanation:
 The correct hierarchy is to place broad topics at the top, with more specific topics below. This structure makes it easier to locate files based on general categories first and then drill down into more detailed subfolders.

33. A data analyst creates a spreadsheet with five tabs. They want to share the data in tabs 1-4 with a client. Tab 5 contains private information about other clients. Which of the following tactics will enable them to keep tab 5 private? Select all that apply.

  • Rename tab 5 to include the word “private” then share the spreadsheet with the client.
  • Hide tab 5, then share the spreadsheet with the client.
  • Copy tabs 1-4 into a separate spreadsheet, then share the new file with the client. ✅
  • Make a copy of the spreadsheet, delete tab 5, then share the new file with the client. ✅

Explanation:
Hiding tab 5 or renaming it as “private” does not protect the data—it can still be accessed by anyone with file access. The best approach is to create a separate file or delete the sensitive tab before sharing.

Part 1 (Q1–15)

Congratulations! You’ve completed all questions. Share this post if it helped you, and check out other Coursera quiz answers below.

Related contents:

Module 1: Data types and structures
Module 2: Data responsibility
Module 3: Database essentials
Module 6: *Course challenge*​

You might also like:

Course 1: Foundations: Data, Data, Everywhere
Course 2: Ask Questions to Make Data-Driven Decisions
Course 4: Process Data from Dirty to Clean
Course 5: Analyze Data to Answer Questions
Course 6: Share Data Through the Art of Visualization
Course 7: Data Analysis with R Programming
Course 8: Google Data Analytics Capstone: Complete a Case Study

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