Designing so data and workloads can move across clouds or regions

Designing for portability means choosing architectures and services that let workloads and data move across clouds or comply with regional residency laws without major re-engineering. Google Cloud supports this through open-source-based managed services, containerization, and multi-cloud data analytics tools.

Must-know

BigQuery Omni lets you run BigQuery analytics on data stored in AWS S3 or Azure Blob Storage without moving the data, addressing multi-cloud and residency needs.
GKE and Anthos (Google Distributed Cloud) enable running containerized workloads consistently across on-premises, Google Cloud, AWS, and Azure, avoiding vendor lock-in.
Dataproc and Dataflow are built on open-source (Apache Hadoop/Spark, Apache Beam) so pipelines can be ported to other environments running the same open-source frameworks.
Data residency requirements can be met using organization policies to restrict resource locations, regional (not multi-regional) storage buckets/datasets, and Assured Workloads for regulated industries.
Using open formats (Parquet, Avro) and open APIs (rather than proprietary formats) reduces lock-in risk and simplifies migration between clouds.
For data residency, storing data in a specific region does not automatically prevent processing outside it, compute location and network egress must also be explicitly controlled via org policies and VPC Service Controls.

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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.