1. Question 1

Bread → Butter pattern. Which ML technique?

• ❌ Classification

• ❌ Regression

• ❌ Clustering

• ✅ Association technique

Explanation:
Association rule mining (like Apriori) is used for market basket analysis.

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2. Question 2

Correct machine learning lifecycle workflow?

• ❌ Prepare data → Design UI → Train model → Collect feedback

• ✅ Define problem → Collect data → Prepare data → Develop & evaluate model → Deploy model

• ❌ Prepare model → Clean model → Visualize model → Report model

• ❌ Define problem → Prepare data → Deploy model

Explanation:
This is the complete, widely accepted ML pipeline.

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3. Question 3

Removing missing values and formatting dates is:

• ❌ Data evaluation

• ❌ Problem definition

• ❌ Data cleaning

• ✅ Data preparation

Explanation:
Data preparation includes cleaning, transforming, formatting, encoding, etc.

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4. Question 4

Python tool for customizing plots?

• ✅ Matplotlib

• ❌ Scikit-learn

• ❌ Python

• ❌ Pandas

Explanation:
Matplotlib is a dedicated plotting library.

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5. Question 5

Which library lets you split data & train classifiers?

• ❌ NumPy

• ✅ Scikit-learn

• ❌ Pandas

• ❌ Matlabplot

Explanation:
Scikit-learn provides train_test_split, classifiers, metrics, pipelines, etc.

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6. Question 6

Tool for storing & querying massive datasets?

• ❌ TensorFlow

• ❌ PostgreSQL (good DB but not for large-scale distributed big data)

• ❌ NumPy

• ❌ PyTorch

• Correct Answer:
  ✅ PostgreSQL (Because the question asked: “storing & querying massive datasets efficiently” → SQL database)

Explanation:
PostgreSQL is used for large-scale structured data storage + efficient querying.

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7. Question 7

Function to split dataset for training/testing in Scikit-learn?

• ❌ It scales your data

• ✅ It splits your dataset into training and testing subsets

• ❌ It generates a classification model

• ❌ It evaluates model accuracy

Correct Function:
train_test_split()

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🧾 Summary Table