Module 4: Verify and Report on Cleaning Results Answers (Part 3: Q31–46)

This is Part 3 of the Module 4 quiz answers for “Verify and Report on Cleaning Results” from the Google Data Analytics Professional Certificate on Coursera.

Here, we’ll walk through questions 31 to 46 with detailed explanations to support your learning.

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

31. A data analyst is in the verification step. They consider the business problem, the goal, and the data involved in their analytics project. What scenario does this describe?

  • Reporting on the data
  • Considering the stakeholders
  • Seeing the big picture ✅
  • Visualizing the data

Explanation:
Seeing the big picture means evaluating how data cleaning supports broader business objectives and aligns with project goals.

32. During data cleaning, you find an error in a username where the ID number was accidentally joined to the user’s last name. You need to figure out if this username has been entered incorrectly more than once in your datasett. If you use a pivot table, what function can you use to determine the number of times this error occurs in your dataset?

  • CASE
  • COUNT
  • COUNTA ✅
  • CHECK

Explanation:
COUNTA counts non-empty values, making it useful for identifying how often a specific error appears in a dataset.

33. You’re working with a dataset that contains categorical variables. You notice that some of the strings are misspelled or are not capitalized. What function can you use to fix these errors when a condition is met?

  • ELSE
  • CASE ✅
  • WHEN
  • THEN

Explanation:
The CASE function can apply specific formatting or corrections when certain conditions (like misspellings) are met.

34. A data analyst uses a changelog while cleaning data. What process does a changelog support?

  • Illumination
  • Examination
  • Disclosure
  • Documentation ✅

Explanation:
A changelog is a key tool in documentation, helping track what, why, and when changes were made during data cleaning.

35. A changelog is essential for storing chronological modifications made during the data cleaning process. When will an analyst refer to the information in the changelog to certify data integrity?

  • Documentation
  • Verification ✅
  • Presenting
  • Visualization

Explanation:

  • During verification, analysts use the changelog to confirm that cleaning processes were executed properly and data integrity is maintained.

36. Fill in the blank: As a data analyst, you should always create a _____ to track your additions, deletions, errors, and changes to a query.

  • notepad
  • database
  • changelog ✅
  • spreadsheet

Explanation:
A changelog is essential for tracking all significant changes, enabling transparency and accuracy in the analysis process.

37. Fill in the blank: TRIM is a function that removes _____ spaces in data. Select all that apply.

  • repeated ✅
  • trailing ✅
  • leading ✅
  • inner

Explanation:
TRIM eliminates leading, trailing, and sometimes repeated spaces, ensuring clean and uniform data entries.

38. While verifying cleaned data, a data analyst encounters a misspelled name. Which function can they use to determine the number of misspelled occurrences in the dataset?

  • CASE
  • CHECK
  • CHECK
  • COUNTA ✅

Explanation:
COUNTA counts the number of non-blank values, helping to identify how many times a specific misspelled name appears.

39 At what point during the analysis process does a data analyst use a changelog?

  • While cleaning the data ✅
  • While visualizing the data
  • While gathering the data
  • While reporting the data

Explanation:
A changelog is actively used during data cleaning to track all modifications made to the dataset and queries.

40. Your manager points out an error in a product ID number in your dataset. The Product IDs can be numbers like 42 or text like "CAD-425". Using a pivot table, what function can you use to find how many times this error occurs in the dataset?

  • COUNT
  • CHECK
  • COUNTA ✅
  • CASE

Explanation:

  • The COUNT function tallies occurrences of specific values, making it suitable for identifying errors in datasets.

41. While reviewing your coworker’s data cleaning process, you find a few cases of trailing spaces in the data. What function can you use to remove these spaces?

  • REMOVE TRAILING
  • DELETE
  • CUT
  • TRIM ✅

Explanation:
TRIM cleans both leading and trailing spaces, making it effective for removing extra characters in textual data.

42. Which of the following queries considers one or more conditions and returns a value as soon as that condition is met?

  • SELECT * WHEN CASE COLUMN = VARIABLE
  • SELECT * CASE IF COLUMN = VARIABLE
  • SELECT * CASE WHEN COLUMN = VARIABLE ✅
  • SELECT * IF CASE COLUMN = VARIABLE

Explanation:
The CASE WHEN structure is correct for evaluating conditions and returning values accordingly in SQL queries.

43. Fill in the blank: Once data is clean, a data analyst moves on to _____ and verification.

  • processing
  • confirming
  • publishing
  • reporting ✅

Explanation:
After cleaning, the analyst conducts reporting to present insights and verification to ensure accuracy.

44. A data analyst is starting a large scale project. The project will be crucial to business success and the data analyst needs to keep the big picture at the forefront when verifying their data cleaning. What is the first step in the verification process?

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

Explanation:
Verification begins by comparing cleaned data with the original to ensure changes were appropriate and accurate.

45. You use SQL to clean your data. You make comments whenever you modify your queries to keep track of any changes. What documentation will this practice help you create when you’re done cleaning the data?

  • A changelog ✅
  • A query repository
  • A new dataset
  • A database

Explanation:
Commenting on query modifications builds a strong changelog, supporting transparency and understanding of changes.

46. A data analyst is starting a large scale project that is crucial to business success. The data analyst needs to remember the big picture when verifying their data cleaning. What is involved when focusing on the big picture-view of the project? Select all that apply.

  • Consider the reporting
  • Consider the business problem ✅
  • Consider the stakeholders
  • Consider the goal ✅

Explanation:

  • Business problem: Ensures the cleaning aligns with solving the primary issue.
  • Goal: Keeps the project’s objective in perspective during verification.

Consider the reporting and Consider the stakeholders are not directly part of focusing on the big picture but rather outcomes of data analysis.

Part 2 (Q16–30)

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: The importance of integrity
Module 2: Clean data for more accurate insights
Module 3: Data cleaning with SQL
Module 6: Course wrap-up

You might also like:

Course 1: Foundations: Data, Data, Everywhere
Course 2: Ask Questions to Make Data-Driven Decisions
Course 3: Prepare Data for Exploration
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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