An enterprise e-commerce platform processes terabytes of raw clickstream events daily. The architecture ingests data into Cloud Storage and loads it into BigQuery for analytical processing. The FinOps team identifies escalating storage and compute expenses and establishes the following requirements: Cloud Storage: Raw event objects must remain in high-performance storage for 30 days, transition to a lower-cost tier for infrequent monthly access between days 31 and 90, and move to the lowest-cost archival tier after 90 days for long-term compliance retention. BigQuery Storage & Query Optimization: Analytical queries primarily filter on daily event timestamp boundaries and frequently filter or aggregate by specific user IDs and event types. Full table scans must be eliminated to drastically curb processed byte volumes. BigQuery Compute Cost Management: The team requires predictable billing limits and wants to prevent runaway query costs caused by concurrent ad-hoc analysis, replacing unpredictable on-demand processing fees. Which combination of actions should the DevOps engineer implement to meet these architectural and financial goals?

Configure Object Lifecycle Management rules on the Cloud Storage bucket to transition objects to Nearline storage after 30 days and to Archive storage after 90 days; partition the BigQuery table by DATE(event_timestamp) and cluster by user_id and event_type; create an autoscaling slot reservation under BigQuery editions capacity-based billing with an assigned slot cap.: ### What the Recommended Solution Is This solution implements an integrated **FinOps data optimization strategy** that automates storage lifecycle transitions in **Cloud Storage**, reduces query data scanning via **BigQuery partitioning and clustering**, and enforces predictable compute spending using **BigQuery capacity-based slot reservations**. ### How It Meets All Requirements - **Meets Cloud Storage Lifecycle Requirement**: Applying **Object Lifecycle Management** with age-based conditions automatically transitions active raw objects to **Nearline storage** at 30 days (optimized for access less than once a month) and to **Archive storage** at 90 days (optimized for long-term cold compliance archiving at the lowest storage cost). - **Meets Query Optimization Requirement**: Defining table partitioning on `DATE(event_timestamp)` enables partition pruning, eliminating scans on dates outside query filter clauses. **Clustering** on `user_id` and `event_type` co-locates related data within partitions, further reducing the byte blocks read during analytical queries. - **Meets Compute Cost Management Requirement**: Transitioning from on-demand billing (which bills per TiB scanned) to **BigQuery editions capacity-based billing** with **slot reservations** and maximum slot caps establishes predictable compute budgets and prevents runaway on-demand query charges. ### Key Advantages and Capabilities - **Automated Storage Tiering**: Zero operational overhead required for ongoing data aging and class transitions. - **Drastic I/O Reduction**: Combines coarse-grained partition pruning with fine-grained cluster block pruning to slash processed data sizes. - **Budget Predictability**: Dedicated slot allocations prevent spiky ad-hoc queries from exceeding budget thresholds. - **Separation of Concerns**: Independently scales and controls data storage expenses and computational query workloads. ### Why This Is Optimal This architecture directly aligns infrastructure utilization with data access patterns. It avoids manual batch operations or complex sharding anti-patterns while maximizing cost efficiency across the complete data lifecycle.