Module 1 challenge :The Nuts and Bolts of Machine Learning (Google Advanced Data Analytics Professional Certificate) Answers 2025

.

1. Supervised vs Unsupervised ML

✔ In unsupervised machine learning, data professionals ask the model to give them information without telling the model what the answer should be.
✔ Supervised machine learning uses labeled datasets to train algorithms to classify or predict outcomes.
✔ Data professionals use supervised machine learning for prediction.
❌ In supervised machine learning, data professionals ask the model to give them information without telling the model what the answer should be.

2. Machine learning and _____

❌ quality assurance
❌ coding
❌ reinforcement learning
✔ artificial intelligence

3. Continuous variables

❌ The number of pallets on a truck
✔ The weight of a concrete block
✔ The age of a building
✔ The height of a skyscraper

4. Content-based filtering

❌ Content-based filtering properties never have to be selected and mapped manually.
✔ Content-based filtering can go beyond comparing items to recommending other things that match a user’s preferences.
✔ Content-based filtering does not require information from other users to work properly.
❌ Content-based filtering is ineffective at making recommendations across content types.

5. Collaborative filtering drawback

❌ redundant
❌ inaccurate
✔ missing
❌ conflicting

6. PACE stage

❌ Analyze
❌ Construct
✔ Plan
❌ Execute

7. Python notebooks vs scripts

❌ Python scripts are useful for pairing code with human-readable descriptions and outputs.
❌ Python notebooks are executed by a computer without the need for human supervision.
✔ Data professionals can use both Python notebooks and scripts to execute code.
✔ Data professionals often alternate between Python notebooks and scripts.

8. Visualization package for dashboards

❌ HTML
❌ Matplotlib
✔ Plotly
❌ Tableau

9. Department for hardware/software help

✔ information technology
❌ business intelligence
❌ marketing
❌ sales

10. Reasons for model bias

✔ The data used to train it could be biased.
✔ Too much emphasis could be placed on accuracy instead of fairness.
❌ The dataset used to train the model might be too big.

✅ Summary Table