Case study

Contoso Retail — semantic product search

Contoso Retail is replacing keyword product search with a semantic search service for a catalogue of 4 million SKUs.

The ingestion pipeline embeds each product description with a 1536-dimension model and writes the vector alongside the product record. Catalogue updates arrive continuously from three regional merchandising teams, and roughly 40,000 products change on a normal weekday.

The storefront queries the service on every keystroke after a 200 ms debounce. Peak traffic is 12,000 queries per second on promotional days, and 95% of those queries hit the same 2,000 popular products. Product records are read far more often than they are written.

The search API must return results in under 150 ms at the 99th percentile. Search is regarded as revenue-critical: a regional outage must not take the storefront down.

Requirements

Vector similarity search over 4 million embeddings
Sub-150 ms p99 read latency for the storefront
Continuous ingestion without blocking reads
Survive the loss of a single Azure region
Control cost — the team flagged RU consumption as a concern

Q44 Cosmos DB Domain 2 · 25–30% of exam Priority · Real Exam

Which partition key best fits the described read and write pattern for the product container?

A A single fixed value so all products share one partition
B /productId, giving high cardinality and even distribution (correct answer)
C /region, matching the three merchandising teams
D /lastUpdatedDate

Shortcut: Point reads and vector lookups are per product, and /productId has cardinality of 4 million with even distribution. /region creates three enormous partitions and a hot one; a date key makes today's partition hot for every write; a fixed value is a single logical partition capped at 20 GB.

Which partition key best fits the described read and write pattern for the product container?

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