Picking a way to move existing data into Google Cloud
Google Cloud offers multiple data transfer tools depending on data source, volume, and network constraints, and the exam expects you to match the right tool to the right scenario. Storage Transfer Service handles online transfers from other cloud providers, on-premises HTTP/S sources, and between Cloud Storage buckets, while Transfer Appliance is a physical device for offline transfer of very large on-premises datasets. Choosing correctly hinges on data size, source/destination type, network bandwidth, and whether the transfer needs to be recurring or one-time.
Must-know
- Storage Transfer Service is used for large-scale online transfers from AWS S3, Azure Blob Storage, on-premises HTTP/S/POSIX sources, and Cloud Storage-to-Cloud Storage moves, and supports scheduled/recurring syncs.
- Transfer Appliance is a physical, ruggedized storage device shipped to your site for offline transfer of very large datasets (typically 100TB+) when network bandwidth is insufficient or transfer time over the network would be impractical.
- gcloud storage or gsutil (the CLI tools) are appropriate for smaller, ad hoc, one-time transfers where the data volume and time constraints don't justify a managed transfer service.
- Storage Transfer Service for on-premises data requires installing a transfer agent on-premises to facilitate the online transfer.
- The choice depends on network bandwidth versus data size: if the estimated online transfer time is too long or costly given available bandwidth, Transfer Appliance is the better choice.
- Storage Transfer Service is fully managed, offers built-in scheduling, filtering, and retry logic, making it preferable over manual CLI-based copying for large or recurring migrations.
A retail company must migrate 1.5 PB of historical sales data from an on-premises storage array to Cloud Storage. The data center's internet uplink is shared with production systems and provides only 150 Mbps of usable bandwidth, and management wants the migration finished within three weeks. Which data transfer approach should the data team choose?
What you have tried across GCP ADP's objectives, not a readiness score.
Data Preparation and Ingestion
- When to load first and when to transform first, and what sits between the two
- Picking a way to move existing data into Google Cloud
- Judging whether a dataset is trustworthy enough to build on
- Fixing messy records before they reach a report
- Telling CSV, JSON, Parquet, Avro, and relational tables apart, and where each fits
- Picking how to pull data out of a source system
- Matching a workload to the right storage or database service
- Getting files and tables loaded with a CLI, a transfer service, or a client library
Data Analysis and Presentation
- Writing BigQuery SQL that answers a reporting question
- Exploring and charting data inside a hosted notebook
- Turning a question from the business into an analysis that settles it
- Building a dashboard and getting it in front of the right people
- Deciding whether a job calls for Looker or for Looker Studio
- Editing LookML to change what a model exposes
- Spotting a problem worth solving with BigQuery ML or AutoML
- Calling a hosted Google language model straight from BigQuery
- Sequencing a machine learning project from raw data to served predictions
- Building, fitting, and scoring a model with SQL alone
- Running predictions against a model you already trained
- Keeping trained models catalogued in one place
Data Pipeline Orchestration
- Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
- Weighing whether the transform belongs before or after the load
- Assembling the services a simple transformation pipeline needs
- Putting a query on a schedule and keeping it running
- Watching a Dataflow job and spotting where it stalls
- Reading logs and metrics to work out what a pipeline actually did
- Choosing what should drive a multi-step workflow
- Streaming messages into BigQuery as they arrive rather than in batches
- Wiring a trigger so one event starts the next step
Data Management
- Granting only the access a person or service actually needs
- Controlling who can read a bucket, and what uniform access changes
- Sharing a dataset with another team or company without copying it
- Matching a storage class to how often the data gets read
- Expiring old data automatically so it stops costing money
- Picking somewhere to park data that must be kept but is rarely read
- Comparing the managed backup and restore options across services
- Working out when a second copy is worth what it costs
- Regions, dual-regions, multi-regions, and zones as redundancy choices
- Deciding who should hold the encryption keys
- What a key management service does for creating, rotating, and revoking keys
- Protecting data on the wire versus data sitting on a disk
Coverage checked against the published exam guide on Aug 12, 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.