Module 1: Ask Effective Questions Answers (Part 4: Q46–63)

This is Part 4 of the Module 1 quiz answers for “Ask Effective Questions” from the Google Data Analytics Professional Certificate on Coursera.

Here, we’ll walk through questions 46 to 63 with detailed explanations to support your learning.

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

46. The question, “Why was the Monday afternoon yoga class successful?” is not measurable. Which of the following questions presents a measurable way to learn about the yoga class?

  • Why do people like taking yoga classes on Mondays?
  • How many customers responded to our recent half-price yoga promotion? ✅
  • Is yoga a great way to stretch and strengthen your body?
  • Do yoga instructors seem more energetic at the beginning of the week?

Explanation:
Measurable questions involve quantifiable answers. Asking “how many” gives you numerical data to analyze, unlike vague or opinion-based questions.

47. Why should a data analyst only ask fair questions?

  • Unfair questions do not have answers.
  • Unfair questions can provide data that is misleading. ✅
  • Fair questions are biased.
  • Fair questions do not offend people.

Explanation:
Unfair questions skew responses and result in biased or inaccurate data, making it unreliable for analysis or decision-making.

48. In the share step of the data analysis process, a data analyst summarizes their results using data visualizations and creates a slideshow to present to stakeholders. What else might they do in this step?

  • Collect data.
  • Communicate findings. ✅
  • Organize the available information
  • Shred paper files.

Explanation:
The share step is about presenting the results:

  • Through visualizations
  • Summarizing findings
  • Communicating with stakeholders

49. If a cooking supply store wants to attract more customers, where can they advertise to better reach their target audience? Select all that apply.

  • On TV during the season finale of The Best Chef in the Universe ✅
  • At a bus stop near a local culinary school ✅
  • On a podcast for foodies ✅
  • In a magazine all about advertising

Explanation:
These are all platforms or places where people interested in cooking are likely to be. It’s effective targeting.

50. Making predictions is one of the six data analytics problem types. How does data factor into such problem types?

  • The data informs the predictions. ✅
  • The data confirms the decisions.
  • The data are the predictions.
  • The predictions validate the data.

Explanation:
Predictions are based on patterns, trends, and insights derived from data. Analysts use historical and current data to forecast future outcomes.

51. Which of the following examples are closed-ended questions? Select all that apply.

  • How tall are you? ✅
  • What did you think about the article that I sent you?
  • What is your opinion of the new movie?
  • Have you taken this class before? ✅

Explanation:
Closed-ended questions are those that can be answered with a specific response, often yes/no or a single value. Open-ended questions, in contrast, require elaboration or detailed responses.

52. What is the defining characteristic of measurable questions?

  • They are questions that have numbers in them.
  • Their answers are numbers that can be interpreted qualitatively.
  • They are questions that use numbers as categories.
  • Their answers are numbers that can be interpreted mathematically. ✅

Explanation:
Measurable questions produce quantitative data that can be analyzed mathematically, allowing for objective evaluation of metrics and outcomes.

53. Fill in the blank: “How many people filled out the survey?” is an example of a question that is _____ in the context of data analysis.

  • categorical
  • symbolic
  • measureable ✅
  • qualitative

Explanation:
It asks for a specific number, making it measurable and useful for quantitative analysis.

54. Fill in the blank: In the _____ step of the data analysis process, an analyst would create visualizations to summarize their results.

  • process
  • share ✅
  • prepare
  • act

Explanation:
Creating visualizations and presenting results to others happens in the share step — the final stage of the data analysis process.

55. A community college wishes to share information about their new career technical degrees. Who are likely examples of their target audience? Select all that apply.

  • Students newly enrolled at a state university
  • People who are happy with their current jobs
  • People looking for a career change ✅
  • Students who just graduated high school ✅

Explanation:
These groups are actively considering education options, making them the ideal audience for career technical programs.

56. A restaurant is considering offering a delivery option for its customers. They use data to forecast the demand for this service. This is an example of which problem type?

  • Spotting something unusual
  • Identifying themes
  • Discovering connections
  • Making predictions ✅

Explanation:
This involves using data to predict future behavior or demand, which is central to predictive analytics.

57. Fill in the blank: The question, “How could we improve our website to simplify the returns process for our online customers?” is _____-oriented.

  • action ✅
  • passive
  • data
  • bias

Explanation:
Action-oriented questions are aimed at solutions or improvements, driving next steps in business or design.

58. Why is reaching your target audience important in data analysis?

  • It brings awareness of your products to potential customers. ✅
  • It makes your products easier to use for your customers.
  • It improves customer service for those currently using your products.
  • It increases the effectiveness of your services for customers.

Explanation:
Understanding your target audience ensures that data insights are applied to enhance the relevance and effectiveness of products or services, ultimately improving customer satisfaction and outcomes.

59. Making predictions is one of the six data analytics problem types. It deals with using data to inform decisions about how things might be in the future. Select the scenario that’s an example of making predictions.

  • A data analyst at a gas company uses historical data to analyze a fluctuation in gas usage.
  • A data analyst at a school system uses data to make a connection between home sales and new student enrollment.
  • A data analyst at a shoe retailer uses data to inform the marketing plan for an upcoming summer sale. ✅
  • A data analyst at a technology company uses data to identify a unique drop in social media engagement.

Explanation:
They’re using data to plan future actions, which is a prediction.

60. Fill in the blank: Questions that make assumptions or suggest that a given answer is correct are examples of _____ questions.

  • unbiased
  • fair
  • wrong
  • unfair ✅

Explanation:
These types of questions bias the respondent, leading to unreliable data.

61. In structured thinking, why would a data analyst organize the available information?

  • To recognize the current problem or situation ✅
  • To consult with subject matter experts
  • To ask SMART questions
  • To summarize results using data visualizations

Explanation:
Organizing information helps a data analyst clearly understand and define the problem or situation they are addressing. This is the first step in structured thinking and ensures a focused approach to analysis.

62. While creating data visualizations for a slideshow, a data analyst considers, “What would help a stakeholder understand this data better?” The analyst is in the analyze step of the data analysis process.

  • True
  • False ✅

Explanation:
This action is part of the share step in the data analysis process. It focuses on presenting data in a way that stakeholders can easily understand and use to make decisions. The analyze step involves examining data to identify trends, patterns, or insights.

63. In data analysis, identifying themes involves which of the following?

  • Creating new classifications for items
  • Grouping categories into broader themes ✅
  • Creating labels for items
  • Bringing different items back together in a single group

Explanation:
Identifying themes requires categorizing data points into broader, more general themes to facilitate understanding and analysis of trends or patterns.

Part 3 (Q31–45)

Congratulations! You’ve completed all 63 questions. Share this post if it helped you, and check out other Coursera quiz answers below.

Related contents:

Module 2: Make data-driven decisions
Module 3: Spreadsheet magic
Module 4: Always remember the stakeholder
Module 4: *Course challenge*

You might also like:

Course 1: Foundations: Data, Data, Everywhere
Course 3: Prepare Data for Exploration
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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