Module 1: Ask Effective Questions Answers (Part 1: Q1–15)

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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.

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:

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.

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

Next Part: Module 1: Ask Effective Questions Answers (Part 2: Q16–30)

Part 2: Q16–30

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