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Expected answer

Choose MongoDB for: rapidly evolving schemas, document-oriented data (CMS, product catalogs), high write throughput with eventual consistency, horizontal scaling needs, and real-time analytics with aggregation pipelines. Choose PostgreSQL for: strict ACID transactions, complex relational joins, financial/regulated data, mature tooling ecosystem, and when you need row-level security or advanced SQL features. Many modern applications use both: PostgreSQL for transactional data and MongoDB for flexible document storage or caching layers.

Expected answer

Embed data that is accessed together: product with its variants, reviews with ratings summary. Reference data that changes independently: user profiles, order history. Denormalize for read performance: store product name in order items (avoid joins). Use the bucket pattern for time-series data (orders per day). Shard by a high-cardinality field (user_id or region). Key anti-patterns to avoid: unbounded array growth (reviews should be in a separate collection after ~100), deep nesting (max 2-3 levels), and treating MongoDB like a relational database with excessive normalization.

Expected answer

Aggregation pipelines process documents through sequential stages: $match (filter), $group (aggregate), $project (reshape), $lookup (join), $unwind (flatten arrays), $sort, $limit. Real-world example for a sales dashboard: $match orders in date range, $unwind line items, $group by product_id to sum revenue and count, $sort by revenue descending, $lookup product details, $project final fields. Optimization: put $match early to filter data, use indexes that support the $match stage, and avoid $lookup on large collections (denormalize instead).