1. Question 1

Machine learning is iterative.

• ✅ True

• ❌ False

Explanation:
ML requires many experiments and refinements; first attempts rarely work perfectly.

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2. Question 2

Correct ML workflow order:

• (ii) Collect data with A & B

• (iii) Train ML model

• (i) Deploy and get user feedback

→ ✅ (ii) (iii) (i)

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3. Question 3

Key steps in a Data Science project:

• ❌ Collect data

• ❌ Analyze data

• ❌ Suggest hypothesis/actions

• ✅ All of the above

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4. Question 4

Machine learning can help with (Select ALL):

• ✅ Customize product recommendations

• ✅ Automate resume screening

• ✅ Automate visual inspection

• ✅ Automate lead sorting

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5. Question 5

You need Big Data to do ML:

• ❌ True

• ✅ False

Explanation:
ML can work well even with small/medium datasets.

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6. Question 6

Technical diligence includes: (Select ALL)

• ❌ Defining an engineering timeline

• ✅ Making sure you can get enough data

• ❌ Estimating business ROI (business diligence)

• ✅ Ensuring ML can meet performance requirements

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7. Question 7

Business diligence:

• ❌ Takes less than a day

• ❌ Only for new product lines

• ✅ Ensures the AI solution is valuable to the business

• ❌ Ensures feasibility (that’s technical diligence)

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8. Question 8

Training set for supervised learning: (Select ALL)

• ❌ Only input A needed

• ✅ Input A and output B must be included

• ❌ Training and test can be same dataset

• ✅ Used to train ML algorithm

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9. Question 9

Test set for resume screening: (Select ALL)

• ✅ Used to evaluate algorithm performance

• ❌ Does not need outputs

• ✅ Should include A and B

• ❌ Should be identical to training set

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10. Question 10

Why ML can’t reach 100% accuracy?

• ❌ Not enough data only

• ❌ Mislabeled data only

• ❌ Ambiguous data only

• ✅ All of the above

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🧾 Summary Table

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