Picking somewhere to park data that must be kept but is rarely read
For long-term archival with infrequent access, Cloud Storage's Archive storage class offers the lowest per-GB cost, and Object Lifecycle Management can automatically transition or delete objects based on age or other conditions. Choosing the right storage class and retention approach depends on how often data must be accessed and any compliance-driven immutability requirements.
Must-know
- Cloud Storage Archive class has the lowest storage cost but a 365-day minimum storage duration and higher retrieval cost/latency than Nearline (30-day minimum) or Coldline (90-day minimum).
- Deleting or overwriting an object before its minimum storage duration ends still incurs charges as if it were stored for the full minimum period.
- Object Lifecycle Management rules can automatically transition objects between storage classes (e.g., Standard to Archive) or delete them based on age, number of versions, or custom time predicates, removing the need for manual archival scripts.
- For compliance/immutability requirements, Bucket Lock enforces retention policies as WORM (write-once-read-many), preventing deletion or overwrite until the retention period expires, which is a common requirement in archiving use cases.
- BigQuery automatically discounts a table's storage price to long-term rates when a partition or table has not been modified for 90 consecutive days, with no change in performance or query behavior, so it can serve as an archival option for structured data still queried occasionally.
- For large-scale one-time or bulk data migrations into archival storage, Storage Transfer Service (for online sources) or Transfer Appliance (for offline, large-volume transfers) are the recommended tools rather than manual uploads.
A financial services firm must retain audit logs for 10 years to satisfy a regulatory requirement. The logs are almost never accessed after the first 60 days, and when they are accessed, a retrieval delay of several hours is acceptable. The firm wants to minimize ongoing storage costs for these logs in Cloud Storage while still meeting the retention requirement. Which storage class should the firm select?
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.