Module 1: Ask effective questions

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In this post, I provide accurate answers and detailed explanations for Module 1: Ask effective questions of Course 2: Ask Questions to Make Data-Driven Decisions – 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.

Optional: Familiar with data analytics? Take our diagnostic quiz

Practice Quiz

1. Optional speed track for those experienced in data analytics

The Google Data Analytics Certificate provides instruction and feedback for learners hoping to earn a position as an entry-level data analyst. While many learners will be brand new to the world of data analytics, others may be familiar with the field and simply wanting to brush up on certain skills.

If you believe this course will be primarily a refresher for you, we recommend taking this practice diagnostic quiz. It will enable you to determine if you should follow the speed track, which is an opportunity to proceed to Course 3 after taking each of the Course 2 Weekly Challenges and the overall Course Challenge. Learners who earn 100% on the diagnostic quiz can treat Course 2 videos, readings, and activities as optional. Learners following the speed track are still able to earn the certificate.

Get ready to take the next step in your data analytics journey with the question below!

Categorizing things is one of the six problem types data analysts solve. This type of problem might involve which of the following actions?

  • Classifying or grouping items ✅
  • Using data to envision how something might happen in the future
  • Analyzing how one action leads to or affects another
  • Noticing something outside of the ordinary

Explanation:
Categorizing problems involve grouping or classifying data into specific categories based on shared characteristics.

2. Finding patterns is one of the six problem types data analysts aim to solve. This type of problem might involve which of the following?

  • Noticing something outside of the ordinary
  • Analyzing how one action leads to or affects another
  • Taking categorized items and grouping them into broader topic areas
  • Identifying trends from historical data ✅

Explanation:
Finding patterns often involves analyzing historical data to identify trends, correlations, or recurring patterns.

3. In the SMART methodology, questions that encourage change are described how?

  • Specific
  • Relevant
  • Time-bound
  • Action-oriented ✅

Explanation:
SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. Questions that drive change are typically action-oriented, encouraging concrete steps.

4. Fill in the blank: In data analytics, qualitative data _____. Select all that apply.

  • is specific
  • is subjective ✅
  • measures numerical facts
  • measures qualities and characteristics ✅

Explanation:
Qualitative data is non-numeric and typically describes qualities, characteristics, or subjective experiences. It is often used in surveys or interviews.

5. In data analytics, how are dashboards different from reports?

  • Dashboards contain static data. Reports contain data that is constantly changing.
  • Dashboards monitor live, incoming data from multiple datasets and organize the information into one central location. Reports are static collections of data. ✅
  • Dashboards provide a high level look at historical data. Reports provide a more detailed look at live, interactive data.
  • Dashboards are used to share updates with stakeholders only periodically. Reports give stakeholders continuous access to data.

Explanation:
Dashboards provide real-time, dynamic insights from multiple sources, while reports are usually static documents containing summarized or historical data.

6. Small data differs from big data in what ways? Select all that apply.

  • Small data is typically stored in a database. Big data is typically stored in a spreadsheet.
  • Small data is effective for analyzing day-to-day decisions. Big data is effective for analyzing more substantial decisions. ✅
  • Small data focuses on short, well-defined time periods. Big data focuses on change over a long period of time. ✅
  • Small data involves datasets concerned with a small number of specific metrics. Big data involves datasets that are larger and less specific. ✅

Explanation:
Small data is typically more specific, limited in scope, and focuses on immediate decisions, while big data deals with larger datasets, often over long periods and with broader insights.

7. Fill in the blank: Some of the most common symbols used in formulas include + (addition), - (subtraction), * (multiplication), and / (division). These are called _____.

  • references
  • operators ✅
  • counts
  • domains

Explanation:
In formulas, operators are symbols that perform mathematical operations such as addition, subtraction, multiplication, and division.

8. In the function =SUM(G1:G35), identify the range.

  • =SUM
  • =SUM(G1)
  • G35
  • G1:G35 ✅

Explanation:
The range in the function =SUM(G1:G35) is the cell range from G1 to G35 that the function will sum.

9. To address a vague, complex problem, a data analyst breaks it down into smaller steps. They use a process to help them recognize the current problem or situation, organize available information, reveal gaps and opportunities, and identify options. What does this scenario describe?

  • Analytical thinking
  • Structured thinking ✅
  • Data-driven decision-making
  • Gap analysis

Explanation:
Structured thinking is a methodical approach to breaking down problems, organizing information, and identifying solutions in a logical, step-by-step manner.

10. Asking questions including, “Does my analysis answer the original question?” and “Are there other angles I haven’t considered?” enable data analysts to accomplish what tasks? Select all that apply.

  • Identify primary and secondary stakeholders
  • Help team members make informed, data-driven decisions ✅
  • Use data to get to a solid conclusion ✅
  • Consider the best ways to share data with others ✅

Explanation:
These questions encourage critical thinking, help ensure the analysis is complete, and lead to more informed decision-making and effective communication of findings.

Test your knowledge on taking action with data

Practice Quiz

11. A data analytics team works to recognize the current problem. Then, they organize available information to reveal gaps and opportunities. Finally, they identify the available options. These steps are part of what process?

  • Categorizing things
  • Making connections
  • Applying the SMART methodology
  • Using structured thinking ✅

Explanation:
Structured thinking is a systematic approach to problem-solving. It involves:

  1. Recognizing the current problem or situation.
  2. Organizing information to identify gaps and opportunities.
  3. Identifying the available options.

This method ensures logical and effective decision-making.

12. In which step of the data analysis process would an analyst ask questions such as, “What data errors might get in the way of my analysis?” or “How can I clean my data so the information I have is consistent?”

  • Analyze
  • Ask
  • Prepare
  • Process ✅

Explanation:
The process step involves cleaning and organizing raw data to ensure it is accurate and consistent. This is where analysts focus on eliminating errors, inaccuracies, and inconsistencies that might affect their analysis.

13. A data analyst has entered the analyze step of the data analysis process. Identify the questions they might ask during this phase. Select all that apply.

  • What is the question I’m trying to answer?
  • How can I create an engaging presentation to stakeholders?
  • How will my data help me solve this problem? ✅
  • What story is my data telling me? ✅

Explanation:
The analyze step focuses on interpreting data to derive insights. Analysts look for patterns, trends, and relationships in the data to answer the question at hand.

  • “How will my data help me solve this problem?” ensures that the data aligns with the business goals.
  • “What story is my data telling me?” helps in crafting a narrative based on the data findings.

14. A data analyst is trying to understand what data to use to help solve a business problem. They’re asking questions such as, “What internal data is available in the database?” and “What outside facts do I need to research?” The data analyst is in which phase of the data analysis process?

  • Act
  • Prepare ✅
  • Share
  • Ask

Explanation:
The prepare step is about gathering and organizing data for analysis. During this phase, analysts identify what data is needed, check its availability, and collect relevant data from internal and external sources.

Test your knowledge on solving problems with data

Practice Quiz

15. A data analyst identifies and classifies keywords from customer reviews to improve customer satisfaction. This is an example of which problem type?

  • Finding patterns
  • Making predictions
  • Categorizing things ✅
  • Spotting something unusual

Explanation:
Classifying keywords into groups or categories is a form of categorizing things. This process involves grouping similar data points to make them easier to analyze or act upon, often to improve processes like customer satisfaction.

16. The spotting something unusual problem type could involve which of the following scenarios?

  • A data insight helps a landscaping company envision what will happen in the future.
  • A data analyst at an arts nonprofit classifies similar data points into groups for further analysis.
  • A data analyst working for an agricultural company examines why a dataset has a surprising and rare data point. ✅
  • A data analyst at a clothing retailer creates a list of common topics, categorizes them, and groups each category into a broader subject area for further analysis.

Explanation:
The spotting something unusual problem type focuses on identifying and analyzing anomalies or outliers in data. For example, a rare or unexpected data point in an agricultural dataset may indicate a potential issue, such as equipment failure or a unique environmental factor.

17. A data analyst at an online retailer works with historical sales data. The analyst identifies repeating trends in the sales data. This is an example of which problem type?

  • Making predictions
  • Identifying themes
  • Categorizing things
  • Finding patterns ✅

Explanation:
Identifying repeating trends in historical sales data is an example of finding patterns. This problem type involves recognizing consistent behaviors, trends, or cycles in data, which can provide insights for decision-making or predictions.

Test your knowledge on crafting effective questions

Practice Quiz

18. A data analyst uses the SMART methodology to create a question that encourages change. This type of question can be described how?

  • Stimulating
  • Action-oriented ✅
  • Results-focused
  • Motivational

Explanation:
In the SMART methodology, action-oriented questions focus on prompting actionable changes or results. These questions are structured to drive measurable and impactful outcomes.

19. A time-bound SMART question specifies which of the following parameters?

  • The era, phase, or period of analysis ✅
  • The metrics or measures related to the analysis
  • The topic or subject of the analysis
  • The desired change the analysis should produce

Explanation:
The “time-bound” aspect of SMART questions ensures that the analysis or goal is tied to a specific timeframe. This allows for tracking progress and achieving results within a defined period.

20. A data analyst working for a mid-sized retailer is writing questions for a customer experience survey. One of the questions is: “Do you prefer online or in-store?” Then, they rewrite it to say: “Do you prefer shopping at our online marketplace or shopping at your local store?” Describe why this is a more effective question.

  • The first question is leading, whereas the second question could have many different answers.
  • The first question is closed-ended, whereas the second question encourages the respondent to elaborate.
  • The first question is vague, whereas the second question includes important context. ✅
  • The first question contains slang that might not make sense to everyone, whereas the second question is easily understandable.

Explanation:
The revised question adds clarity by specifying “online marketplace” and “local store,” helping respondents understand the context. The original question lacks these details, making it less effective for data collection.

21. A data analyst at a social media company is creating questions for a focus group. They use common abbreviations such as PLS for “please” and LMK for “let me know.” This is fair because the participants use social media a lot and are likely to be technically savvy.

  • True
  • False ✅

Explanation:
Using abbreviations or jargon can cause misunderstandings, even if participants are familiar with them. Writing questions with clear, straightforward language ensures inclusivity and reduces the risk of misinterpretation.

*Weekly challenge 1*

Graded Quiz

22. Structured thinking involves which of the following processes? Select all that apply.

  • Revealing gaps and opportunities ✅
  • Recognizing the current problem or situation ✅
  • Organizing available information ✅
  • Asking SMART questions

23. A data analyst creates data visualizations and a slideshow. Which phase of the data analysis process does this describe?

  • Prepare
  • Act
  • Share ✅
  • Process

24. A recycling center that sponsors a podcast about saving the environment is an example of what strategy?

  • Defining the problem to be solved
  • Making recommendations
  • Staying on budget
  • Trying to reach a target audience ✅

25. A data analyst is working for a local power company. Recently, many new apartments have been built in the community, so the company wants to determine how much electricity it needs to produce for the new residents in the future. A data analyst uses data to help the company make a more informed forecast. This is an example of which problem type?

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

26. Describe the key difference between the problem types of categorizing things and identifying themes.

  • Categorizing things involves determining how items are different from each other. Identifying themes brings different items back together in a single group.
  • Categorizing things involves assigning grades to items. Identifying themes involves creating new classifications for items.
  • Categorizing things involves taking inventory of items. Identifying themes deals with creating labels for items.
  • Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes. ✅

27. Which of the following examples are leading questions? Select all that apply.

  • What do you enjoy most about our service? ✅
  • How did you learn about our company?
  • In what ways did our product meet your needs? ✅
  • How satisfied were you with our customer representative? ✅

28. The question, “Why don’t our employees complete their timesheets each Friday by noon?” is not action-oriented. Which of the following questions are action-oriented and more likely to lead to change? Select all that apply.

  • What functionalities would make our timesheet web page more user-friendly? ✅
  • What features could we add to our calendar app as a weekly timesheet reminder to employees? ✅
  • How could we simplify the time-keeping process for our employees? ✅
  • Why don’t employees prioritize filling out their timesheets by noon on Fridays?

29. On a customer service questionnaire, a data analyst asks, “If you could contact our customer service department via chat, how much valuable time would that save you?” Why is this question unfair?

  • It is closed-ended
  • It uses slang words that not everyone can understand
  • It is vague
  • It makes assumptions ✅

30. Organizing available information and revealing gaps and opportunities are part of what process?

  • Identifying connections between two or more things
  • Categorizing things
  • Using structured thinking ✅
  • Applying the SMART methodology

31. The share phase of the data analysis process typically involves which of the following activities? Select all that apply.

  • Summarizing results using data visualizations ✅
  • Communicating findings ✅
  • Creating a slideshow to present to stakeholders ✅
  • Putting analysis into action to solve a problem

32. A company wants to make more informed decisions regarding next year’s business strategy. An analyst uses data to help identify how things will likely work out in the future. This is an example of which problem type?

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

33. Fill in the blank: Categorizing things involves assigning items to categories, whereas _____ takes those categories a step further, grouping them into broader classifications.

  • Making predictions
  • Finding patterns
  • Discovering connections
  • Identifying themes ✅

34. Questions that make assumptions often involve concepts that are formed without evidence. An example of this is an idea that is accepted as true without proof.

  • True ✅
  • False

35. A garden center wants to attract more customers. A data analyst in the marketing department suggests advertising in popular landscaping magazines. This is an example of what practice?

  • Reaching your target audience ✅
  • Collecting customer information
  • Monitoring social media feedback
  • Developing a data analytics case study

36. Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes or classifications.

  • True ✅
  • False

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

  • Is math your favorite subject? ✅
  • What grade did you get on the math test? ✅
  • How old are you? ✅
  • What are your thoughts about math?

38. The question, “How could we improve our website to simplify the returns process for our online customers?” is action-oriented.

  • True ✅
  • False

39. Which of the following questions make assumptions? Select all that apply.

  • Keeping employees engaged is important, isn’t it? ✅
  • Wouldn’t you agree that product A is better than product B? ✅
  • Did you get through to customer service?
  • It must be frustrating waiting on hold for so long, right? ✅

Explanation:
These questions embed assumptions that may not align with the respondent’s experience or opinion. Neutral, unbiased questions are essential to gathering accurate and reliable data.

40. Structured thinking involves recognizing the current problem or situation you’re facing and identifying your options.

  • True ✅
  • False

41. Which of the following examples are leading questions? Select all that apply.

  • How satisfied were you with our customer representative? ✅
  • What do you enjoy most about our service? ✅
  • In what ways did our product meet your needs? ✅
  • How did you learn about our company?

42. On a customer service questionnaire, a data analyst asks, “If you could contact our customer service department via chat, how much valuable time would that save you?” Why is this question unfair?

  • It is closed-ended
  • It uses slang words that not everyone can understand
  • It is vague
  • It makes assumptions ✅

43. Fill in the blank: To apply structured thinking, a data analyst should ______ the available information in order to reveal gaps and opportunities and recognize the current problem or situation.

  • organize ✅
  • communicate
  • share
  • record

44. A national chain of sporting goods stores advertises during popular sporting television broadcasts. This is an example of the company doing what?

  • Reaching its target audience ✅
  • Demonstrating its support for a sports team
  • Defining the problem to be solved
  • Monitoring social feedback

45. In data analysis, categorizing things involves which of the following?

  • Creating new classifications for items and assigning grades to items
  • Assigning items to categories ✅
  • Taking an inventory of items
  • Determining how items are different from each other

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?

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.

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.

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

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

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

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 ✅

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 ✅

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

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.

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

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.

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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