1️⃣ Question 1

Which option is essential for ensuring the relevance and usefulness of the model’s answer?

• ❌ The complexity of the model

• ❌ The availability of data

• ❌ Familiarity of stakeholders with the tool

• ✅ The number of stakeholders involved (Incorrect — not ideal)

Wait — let’s re-evaluate carefully.
The only option that truly matches the methodology is:

✅ Familiarity of stakeholders with the tool produced

Explanation:
If stakeholders understand and can use the model’s output, then the answer becomes relevant and actionable.
Complexity doesn’t ensure relevance, and simply having data doesn’t ensure usefulness.

Correct final answer:
✅ Familiarity of stakeholders with the tool produced

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2️⃣ Question 2

Who are the stakeholders that help make the model’s answer relevant?

• ❌ Data scientists and statisticians

• ❌ Cognos developers

• ✅ Solution owners, marketing, application developers, and IT administration

• ❌ Hospital staff and patients

Explanation:
These cross-functional stakeholders ensure the model’s output becomes useful in business operations.

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3️⃣ Question 3

How do data scientists refine the model after initial deployment & feedback?

• ❌ Complete overhaul

• ❌ Collect more data from controlled groups

• ✅ By incorporating information about participation and possibly refining with detailed pharmaceutical data

• ❌ Automating the intervention program

Explanation:
After deployment, feedback reveals missing factors.
Refinement uses new variables (e.g., participation, pharma details).

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4️⃣ Question 4

How does the methodology ensure continuous improvement?

• ✅ By incorporating feedback and making adjustments

• ❌ Making it rigid

• ❌ Rely only on expert opinions

• ❌ Automate entire process

Explanation:
Data science is iterative, improving with feedback loops.

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5️⃣ Question 5

Primary purpose of storytelling in data science?

• ❌ Impress colleagues

• ❌ Highlight achievements

• ❌ Showcase programming skills

• ✅ Communicate complex findings and insights effectively

Explanation:
Storytelling helps translate analytical findings into business understanding.

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🧾 Summary Table