1. Machine learning and real-time personalization

✔️ Through machine learning, with every click that the user makes, their website experience becomes increasingly personalized.
❌ All users see the same recommendations
❌ ML cannot make personalized suggestions
❌ Analyze credit card transactions (irrelevant)

Explanation:
ML models learn user behavior and adapt recommendations in real time → personalization.

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2. Highly organized and well-defined data

❌ Hybrid data
❌ Unstructured
❌ Semi-structured
✔️ Structured data

Explanation:
Structured data lives in tables with rows/columns. It is highly organized and easily searchable.

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3. Use case showing the power of unstructured data

❌ Historical sales → structured
❌ Weather visualizations → mostly structured
❌ GPS ridesharing → semi-structured
✔️ Analyzing social media posts to identify sentiment

Explanation:
Social media text, images, comments = unstructured data → ML/NLP can extract sentiment.

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4. Database stored in tables, rows, columns

❌ Object DB
✔️ Relational database
❌ Non-relational
❌ XML DB

Explanation:
Table-based storage is the hallmark of relational databases (SQL).

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5. Repository for raw data of any type/volume

❌ Data archive
✔️ Data lake
❌ Data warehouse
❌ Database

Explanation:
A data lake stores raw structured + semi-structured + unstructured data.

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6. Google Cloud’s serverless data warehouse

❌ Vertex AI
❌ Compute Engine
✔️ BigQuery
❌ Cloud Storage

Explanation:
BigQuery = Google Cloud’s fully managed, serverless, scalable data warehouse.

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7. Data from your own customer interactions

❌ Third-party
❌ Second-party
✔️ First-party data

Explanation:
First-party = data collected directly by the business from its own users.

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8. Free-to-use weather datasets

❌ Cloud console
❌ Google Play
❌ App Engine
✔️ Google Cloud Marketplace

Explanation:
Marketplace provides public datasets, including weather, census, economic data.

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9. Stage where raw data becomes ready for insights

❌ Data analysis
✔️ Data processing
❌ Data genesis
❌ Data storage

Explanation:
Processing cleans, transforms, and prepares data for analysis.

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10. What is data governance?

✔️ Setting internal data policies + ensuring compliance with external standards
❌ Collecting/storing
❌ Deleting data
❌ Analyzing data

Explanation:
Data governance = policies, security, compliance, and responsibility management.

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🧾 Summary Table