Module 4: Verify and Report on Cleaning Results Answers (Part 1: Q1–15)

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In this post, I provide accurate answers and detailed explanations for Module 4: Verify and report on cleaning results of Course 4: Process Data from Dirty to Clean – Google Data Analytics Professional Certificate.

Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.

Here, we’ll walk through questions 1 to 15 with detailed explanations to support your learning.

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

Test your knowledge on manual data cleaning

Practice Quiz

1. Making sure data is properly verified is an important part of the data-cleaning process. Which of the following tasks are involved in this verification? Select all that apply.

  • Rechecking the data-cleaning effort ✅
  • Considering whether the data is credible and appropriate for the project ✅
  • Asking stakeholders to check and confirm the data is clean
  • Manually fixing any errors found in the data ✅

Explanation:
The verification process ensures the accuracy and reliability of cleaned data:

  • Rechecking the data-cleaning effort ensures no steps were missed.
  • Considering credibility and appropriateness confirms the data aligns with project requirements.
  • Manually fixing errors is essential to address any issues identified during verification.

Asking stakeholders to confirm the data is clean is not part of verification; it’s more about collaboration and validation, not the cleaning process itself.

2. Fill in the blank: To count the total number of spreadsheet values within a specified range, a data analyst uses the _____ function.

  • COUNTA ✅
  • WHOLE
  • TOTAL
  • SUM

Explanation:

  • The COUNTA function counts non-empty cells in a specified range.
  • Other options like WHOLE, TOTAL, and SUM do not serve this purpose.

3. A data analyst is cleaning a dataset with inconsistent formats and repeated cases. They use the TRIM function to remove extra spaces from string variables. What other tools can they use for data cleaning? Select all that apply.

  • Remove duplicates ✅
  • Find and replace ✅
  • Import data
  • Protect sheet

Explanation:

  • Remove duplicates eliminates repeated records.
  • Find and replace standardizes values or corrects errors.
    Other options like Import data and Protect sheet are unrelated to cleaning.

4. To correct a typo in a database column, where should you insert a CASE statement in a query?

  • As a GROUP BY clause
  • As a FROM clause
  • As an ORDER BY clause
  • As a SELECT clause ✅

Explanation:
The CASE statement is used in the SELECT clause to implement conditional logic, correcting typos by mapping incorrect values to correct ones.

Example:SELECT
CASE
WHEN column_name = 'typ0' THEN 'typo'
ELSE column_name
END AS corrected_column
FROM table_name;

Other clauses like GROUP BY, FROM, and ORDER BY are not suitable for such corrections.

Test your knowledge on documenting the cleaning process

Practice Quiz

5. Why is it important for a data analyst to document the evolution of a dataset? Select all that apply.

  • To recover data-cleaning errors ✅
  • To determine the quality of the data ✅
  • To identify best practices in the collection of data
  • To inform other users of changes ✅

Explanation:

  • Recovering data-cleaning errors: Documentation helps identify and reverse any mistakes.
  • Determining data quality: Tracking evolution highlights improvements or issues in the data’s integrity.
  • Informing other users of changes: Ensures all team members are aware of adjustments, fostering collaboration.

To identify best practices in the collection of data is unrelated to documentation of dataset evolution. It pertains more to data collection processes.

6. Fill in the blank: While cleaning data, documentation is used to track _____. Select all that apply.

  • changes ✅
  • errors ✅
  • bias
  • deletions ✅

Explanation:

  • Changes: Tracking modifications ensures clarity and reversibility if issues arise.
  • Errors: Documenting errors enables learning and prevents recurrence.
  • Deletions: Logging removed data ensures transparency and justifies decisions.

Bias is not typically tracked during cleaning but rather during analysis or interpretation phases.

7. Documenting data-cleaning makes it possible to achieve what goals? Select all that apply.

  • Be transparent about your process ✅
  • Keep team members on the same page ✅
  • Demonstrate to project stakeholders that you are accountable ✅
  • Visualize the results of your data analysis

Explanation:

  • Transparency: Shows how data cleaning was conducted, ensuring trust.
  • Team alignment: Documentation ensures all members understand the methods used.
  • Accountability: Demonstrates responsibility and accuracy to stakeholders.

Visualizing the results of your data analysis is not achieved through documentation of data cleaning. This is part of data visualization or analysis phases.

Module 4 challenge

Graded Quiz

8. Verification and reporting come directly before the data-cleaning process.

  • True
  • False

Explanation:
Verification and reporting occur after the data-cleaning process to confirm its accuracy and prepare data for analysis.

9. What is the first step in the verification process?

  • Compare cleaned data with the original, uncleaned dataset and compare it to what is there now ✅
  • Create a chronological list of modifications made to the data
  • Determine the quality of the data
  • Inform others of your data-cleaning effort

Explanation:
This step helps identify whether errors were introduced or unresolved during the cleaning phase by comparing before and after versions.

10. Which of the following functions automatically remove extra spaces when cleaning data?

  • SNIP
  • REMOVE
  • TRIM ✅
  • CLEAR

Explanation:

  • The TRIM function removes extra spaces, ensuring data is consistent. Other options like REMOVE, SNIP, and CLEAR are not valid cleaning functions.

11. What tool can a data analyst use to figure out how many identical errors occur in a dataset?

  • CASE
  • COUNTA ✅
  • CONFIRM
  • COUNT

Explanation:
COUNTA counts non-empty cells, which can help determine the frequency of specific repeated values or errors.

12. Fill in the blank: A data analyst uses the CASE statement to consider one or more _____, then returns a value.

  • additions
  • conditions ✅
  • identifications
  • changes

Explanation:
CASE allows analysts to apply logic based on conditions, returning different values depending on the input data.

13. What is the process of tracking changes, additions, deletions, and errors during data cleaning?

  • Recording
  • Observation
  • Cataloging
  • Documentation ✅

Explanation:

  • Documentation involves recording all modifications to ensure transparency and trackability.

14. Fill in the blank: While cleaning data, a data analyst can use a changelog to keep a chronological list of changes they make. They can refer to it during the _____ period if there are errors or questions.

  • presenting
  • verification ✅
  • documentation
  • visualization

Explanation:
Changelogs provide a historical reference that can be used during verification to track and validate updates.

15. Reviewing version history is an effective way to view a changelog in SQL.

  • True
  • False ✅

Explanation:
SQL databases do not automatically track version history like spreadsheets; version tracking must be done manually or through audit logs.

That’s it for Part 1! Continue your learning journey with the next set of answers.

Next Part: Module 4: Verify and Report on Cleaning Results Answers (Part 2: Q16–30)

Part 2: Q16–30

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