A data engineering team at a retail organization uploads daily transaction log files into a Google Cloud Storage bucket. To minimize storage costs and comply with internal data retention guidelines, the team needs to automatically delete log files once they are 30 days old. Which method should the team use to configure and manage automated object deletion on the Cloud Storage bucket?

Configure an Object Lifecycle Management rule on the bucket with a Delete action based on the Age condition, and update or clear the bucket's lifecycle configuration if policies change.: ### What Cloud Storage Object Lifecycle Management is **Cloud Storage Object Lifecycle Management** allows administrators and data engineers to define automated rules that apply actions to objects stored within a bucket when specified conditions are met. These rules run automatically in the background without requiring custom code or scheduled jobs. ### How it meets the requirements - **Automated deletion (✓):** Setting an action type of `Delete` combined with an `Age` condition of 30 days ensures that all objects reaching 30 days since creation are automatically scheduled for permanent deletion. - **Storage cost optimization (✓):** Automatically purging old or unnecessary log data prevents unbounded storage growth and reduces ongoing Google Cloud storage expenses. - **Manageability and updates (✓):** If retention policies change or automated deletion needs to be suspended, the bucket's lifecycle configuration can be updated, patched, or reset to remove all existing rules. ### Key advantages and capabilities - **No server management:** Operates completely serverless within Cloud Storage without needing Compute Engine instances or cron schedulers. - **Granular condition matching:** Supports conditions such as `Age`, `CreatedBefore`, `IsLive`, `MatchesStorageClass`, and `NumberOfNewerVersions`. - **Tier transition and deletion:** Can transition objects to colder storage classes (Nearline, Coldline, Archive) before eventual deletion. - **Lifecycle lifecycle reset:** Lifecycle rules can be inspected, updated, or removed entirely via the Google Cloud console, the Google Cloud CLI (`gcloud storage`), or client library APIs by patching the bucket metadata. ### Why this is optimal Native Object Lifecycle Management is the Google Cloud recommended mechanism for managing data retention and automated cleanup on Cloud Storage buckets. It eliminates the operational overhead and failure risks associated with maintaining custom deletion scripts.

Cloud Storage Lifecycle Rule Object Deletion | Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!

FundamentalAssociate Data Practitioner

A media analytics organization stores daily application log files in a Google Cloud Storage bucket. Due to data compliance and cost-optimization policies, the organization requires log files to be automatically deleted once they reach 30 days of age, without requiring manual intervention or custom maintenance scripts. Which Cloud Storage feature should you configure to satisfy this requirement?

Configure rules to delete objects after a specified period to automatically remove unnecessary data and reduce storage expenses (e.g., BigQuery, Cloud Storage)

FundamentalAssociate Data Practitioner

A data engineering team manages a daily-partitioned BigQuery table named telemetry_logs. To optimize storage costs and adhere to data retention guidelines, the team needs to ensure that individual partitions are automatically deleted 60 days after their partition date, while keeping the table definition and more recent partitions intact. Which SQL statement should the team execute to configure this automated retention rule?

FundamentalAssociate Data Practitioner

A data engineer has submitted an Apache Beam data processing pipeline to Cloud Dataflow. During execution, the engineer needs to track the pipeline's lifecycle state (such as Running, Succeeded, or Failed), inspect the interactive visual execution graph to see individual transform stages, and monitor step-level performance metrics. Which interface in the Google Cloud console should the data engineer use to inspect these stages and operational metrics?

FundamentalAssociate Data Practitioner

An analytics team ingests daily transaction records into a staging dataset in Google Cloud BigQuery. Before publishing the data to downstream reporting tables, the team needs to assess data quality by verifying that: Mandatory fields such as order_id contain no null or duplicate values Numeric fields such as order_amount are strictly greater than zero Every customer_id matches an existing record in the master customers table Which approach should the team implement in BigQuery to evaluate data accuracy and identify these discrepancies?

FundamentalAssociate Data Practitioner

A data engineer is troubleshooting an automated batch pipeline that failed unexpectedly during execution. The engineer needs to examine the pipeline's log entries in Google Cloud to isolate the root cause efficiently. The engineer has the following requirements: Restrict the displayed logs to entries with a severity level of ERROR or higher Search for log entries containing the phrase connection_timeout in the message payload Narrow down the results to the specific two-hour window when the job executed Which approach should the engineer use in Cloud Logging to accomplish this?

AdvancedProfessional Cloud Security Engineer

An enterprise e-commerce platform hosted on Google Cloud is protected by an external Application Load Balancer and Google Cloud Armor. During high-traffic promotional events, the authentication and checkout APIs experience distributed brute-force and credential stuffing attacks from automated bots. The security engineering team needs to enforce the following defensive requirements: Track request rates per client using an application session cookie named sid. If any client exceeds 100 requests within a 60-second window, ban that client from sending requests for 600 seconds (10 minutes) and return an HTTP 429 Too Many Requests status code. Seamlessly assess automated traffic and block automated bots scoring below 0.5 using reCAPTCHA Enterprise integration before requests reach backend services. Which configuration strategy in Google Cloud Armor satisfies these requirements?

Associate Data Practitioner

Cloud Storage Lifecycle Rule Object Deletion

Related questions