Key points

• Partition and sort keys define identity, item collections, distribution, and primary access patterns.
• Query targets a partition key; Scan examines a broader item set before filtering.
• Tables and LSIs can serve strong reads; GSIs and streams serve eventually consistent reads.
• Conditional writes, TTL, indexes, and Streams support concurrency, lifecycle, access, and event patterns.

Best-known use cases

• Known-key application reads and writes at scale.
• Tenant-aware, event, session, and idempotency records with explicit lifecycle.

What candidates often confuse it with

• DynamoDB primary-key access differs from Aurora/RDS relational SQL and joins.
• A secondary index adds an access path; a cache reduces repeated access but does not define source identity.

Key takeaway

Design DynamoDB from access patterns, distributed keys, consistency, and conditional behavior rather than scanning after the fact.

Related services

• Amazon ElastiCache
• Amazon OpenSearch Service
• AWS Lambda

Relevant exam tasks

• D1.3 — Use data stores in application development
• 1.3.1 — Describe high-cardinality partition keys for balanced partition access
• 1.3.2 — Describe database consistency models (for example, strongly consistent, eventually consistent)
• 1.3.3 — Describe differences between query and scan operations
• 1.3.4 — Define Amazon DynamoDB keys and indexing
• 1.3.5 — Serialize and deserialize data to provide persistence to a data store
• 1.3.6 — Use, manage, and maintain data stores
• 1.3.7 — Manage data lifecycles

Learn more

• Amazon DynamoDB