Identifying where data comes from and where it needs to land

Defining data sources and sinks means identifying where data originates (e.g., Pub/Sub, Cloud Storage, on-prem databases, streaming apps) and where it lands after processing (e.g., BigQuery, Bigtable, Cloud Storage, Cloud SQL) so pipelines can be designed with correct connectors, schemas, and throughput expectations. Choosing the right source/sink pair drives decisions about pipeline tooling (Dataflow, Dataproc, Data Fusion) and affects latency, cost, and scalability. This is foundational to designing Apache Beam/Dataflow pipelines and other ETL/ELT workflows on Google Cloud.

Must-know

• Pub/Sub is the standard source/sink for streaming ingestion and supports at-least-once delivery with ordering keys for sequencing when needed.
• Cloud Storage is commonly used as a staging sink for batch data and as a durable landing zone before loading into BigQuery or triggering downstream processing via Eventarc/Cloud Functions.
• BigQuery is a frequent sink for analytics workloads and supports both batch loads and streaming inserts, but streaming inserts have different cost and quota behavior than load jobs.
• Dataflow (Apache Beam) natively supports many source/sink connectors (Pub/Sub, BigQuery, Cloud Storage, JDBC, Bigtable) via I/O transforms, and custom sources/sinks can be built using the Beam SDK if a connector doesn't exist.
• When defining sinks, always consider schema compatibility, write disposition (append/overwrite/truncate), and idempotency to avoid duplicate or lost data during retries.
• Datastream and Database Migration Service are used for CDC-based ingestion from operational databases (MySQL, PostgreSQL, Oracle) into sinks like BigQuery or Cloud Storage without invoking custom pipeline code.

Coverage checked against the published exam guide on Aug 11, 2026.

These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.