Translating current and future business needs into an architecture

This objective tests your ability to translate business requirements, current and anticipated, into a Google Cloud data architecture that balances scalability, cost, latency, and governance. You must justify service choices (e.g., BigQuery vs. Bigtable, Dataflow vs. Dataproc, Pub/Sub vs. Cloud Storage) based on data volume, velocity, variety, and access patterns rather than familiarity alone.

Must-know

• Distinguish batch vs. streaming needs early: Dataflow (Apache Beam) unifies both, while Dataproc suits existing Spark/Hadoop workloads needing lift-and-shift with minimal refactoring.
• For analytics at scale with SQL access patterns, BigQuery is the default choice due to serverless scaling and separation of storage/compute, but Bigtable or Spanner are better for high-throughput OLTP or NoSQL key-value access.
• Future scalability requirements should push you toward serverless, auto-scaling services (BigQuery, Dataflow, Pub/Sub) over fixed-cluster solutions (Dataproc) unless there's a strong reason for cluster control.
• Cost and performance trade-offs matter: BigQuery slot reservations vs. on-demand pricing, or Bigtable node count, should be sized against anticipated growth, not just current load.
• Data governance requirements (data residency, lineage, access control) often dictate architecture choices, e.g., using Dataplex, Data Catalog, VPC Service Controls, or CMEK, and must be mapped explicitly to compliance needs stated in the scenario.
• A common exam gotcha: don't over-engineer for hypothetical future scale if the stated requirements only justify a simpler service; conversely, don't choose a service that can't scale if growth is explicitly mentioned as a future requirement.

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.