Graded Quiz: Marketing Analytics in Action :Marketing Analytics with Meta (Meta Marketing Analytics Professional Certificate) Answers 2025
Question 1
When setting a SMART goal, what do the S and M stand for?
✅ Specific and Measurable
❌ Simple and Manageable
❌ Specific and Manageable
❌ Simple and Measurable
Question 2
Blake plans to use likes as a KPI. Is this a good choice?
✅ No
❌ Yes
Question 3
What are some limitations of observational methods? (Choose all that apply)
✅ Doesn’t take contextual variables into account
✅ Might deliver biased outcomes
❌ Tests may not have sufficient statistical power
❌ Very complex and time-consuming
Question 4
What could help to make Blake’s hypothesis stronger?
✅ A time frame they are looking at the behavior
❌ A budget for the test
❌ A goal for this test
❌ A null hypothesis
Question 5
What is the preferred method of multi-touch attribution to get the most valid results?
✅ Data-driven attribution
❌ Direct attribution
❌ Last touch attribution
❌ Model-based attribution
Question 6
Blake wants to start exploring their data. Which of the following is a good place to start?
✅ Descriptive Statistics
❌ Visualizing The Data
❌ Creating a Model
Question 7
What can Blake attempt next after evaluating results against goals and KPIs?
✅ Add perspective and context to the results
❌ Run a different analysis
❌ Propose alternative hypotheses
Question 8
Blake finds that people abandon purchases due to too many checkout fields. What’s the right recommendation?
✅ Update the checkout process and reduce the number of fields.
❌ Make no recommendation
❌ Change the target audience
Question 9
When creating a presentation, what should you focus on first?
✅ Goals, Objectives, and KPIs
❌ Data results
❌ Future plans
❌ Campaign setup
Question 10
How could you open your presentation?
✅ All of the above
(Story, Fact, or Question)
🧾 Summary Table
| Q# | ✅ Correct Answer | Key Concept |
|---|---|---|
| 1 | Specific and Measurable | SMART goal fundamentals |
| 2 | No | Likes ≠ business KPI |
| 3 | Context not included; Bias possible | Observational method limits |
| 4 | A time frame | SMART hypothesis improvement |
| 5 | Data-driven attribution | Most valid multi-touch model |
| 6 | Descriptive Statistics | First step in data exploration |
| 7 | Add perspective and context | Turning results into insights |
| 8 | Update checkout fields | Actionable recommendation |
| 9 | Goals, Objectives, and KPIs | Foundation of presentations |
| 10 | All of the above | Engaging presentation openings |