Expiring old data automatically so it stops costing money
Google Cloud offers automated, policy-based mechanisms to delete or archive stale data without manual intervention, helping control storage costs. Cloud Storage uses Object Lifecycle Management rules on buckets, while BigQuery uses table and partition expiration settings. Both are configured declaratively and enforced by the service in the background.
Must-know
- Cloud Storage Object Lifecycle Management rules are defined per bucket using conditions (e.g., Age, CreatedBefore, NumNewerVersions, IsLive) paired with actions like Delete or SetStorageClass; the rule scans run asynchronously and deletions may take up to 24 hours to execute after conditions are met.
- In BigQuery, you can set a default table expiration at the dataset level or an explicit expirationTime on individual tables; once expired, BigQuery automatically deletes the table and its data with no recovery option beyond time travel/snapshot windows.
- BigQuery also supports partition expiration on partitioned tables, letting individual partitions age out and be deleted independently of the whole table, which is ideal for time-series or log data retention.
- Lifecycle/expiration deletions are permanent (subject to any configured soft-delete or time-travel retention window), so rules should be tested carefully before applying to production data.
- Combining Cloud Storage lifecycle rules with storage class transitions (e.g., moving to Nearline/Coldline/Archive before eventual deletion) is a common cost-optimization pattern rather than deleting immediately.
- These features require no extra compute or scheduling jobs from the user: both Cloud Storage and BigQuery natively enforce the configured rules as part of the managed service.
A data practitioner manages a Cloud Storage bucket that accumulates log files. To reduce storage costs, they want objects to be automatically deleted once they reach 90 days old, without any manual intervention. What should they configure?
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.