Collaboration and Cost Optimization: free practice questions

The Snowflake Marketplace supports trial listings that automatically grant prospective consumers limited access (e.g., time-limited or data-limited) to evaluate the dataset before committing to a purchase, without requiring the provider to manage this manually. Option B (personalized listings) is a real Marketplace concept for customized pricing/data per consumer, but it does not provide automated sample access — it requires manual negotiation and fulfillment. Option C misapplies Data Clean Rooms, which are designed for privacy-preserving joint analysis between collaborating parties, not for marketplace trial access. Option D (Reader Accounts) is a valid sharing mechanism but requires the provider to manually provision and manage an account per prospect, defeating the self-service automation goal.

Transient tables incur no Fail-safe storage costs (Fail-safe is unavailable for transient tables) and support DATA_RETENTION_TIME_IN_DAYS of 0 or 1. Setting retention to 0 eliminates all Time Travel storage overhead for data that is replaced every 24 hours, minimizing cost. Option A uses permanent tables, which carry a mandatory 7-day Fail-safe period, doubling storage charges unnecessarily. Option C is incorrect because temporary tables exist only for the duration of a session; they cannot persist data across pipeline runs or sessions as implied, and they also cannot have a retention period of 1 day in a cross-session context. Option D avoids storage costs but requires querying semi-structured data directly from S3 on every run, losing the performance and caching benefits of native Snowflake storage for intermediate transformations.

Option B is correct: Snowflake allows column-level SOS management. Removing SOS from columns not used in selective lookups reduces both the storage cost of the search access paths and the maintenance compute required to keep them updated. Option D is correct: SOS storage and maintenance costs scale with indexed data volume. Partitioning the table by date and enabling SOS only on the active partition (e.g., last 30 days of fraud-relevant data) significantly reduces the indexed volume while keeping performance for the targeted queries. Option A is incorrect: Clustering keys improve micro-partition pruning for range scans and low-cardinality columns but are not effective for point lookups on high-cardinality columns (e.g., transaction IDs, device fingerprints) — this is precisely the use case SOS is designed for. Option C is incorrect: SOS maintenance cost is driven by data mutations (DML), not by Time Travel retention settings; reducing Time Travel does not lower SOS costs. Option E is fabricated; larger warehouses still use SOS when it is enabled — they do not skip it in favor of full scans.

A Secure View using CURRENT_ACCOUNT() (or CURRENT_ORGANIZATION_ACCOUNT()) as a filter predicate allows the provider to attach multiple consumer accounts to a single Share while enforcing row-level data isolation — each partner automatically sees only the rows relevant to their account. Adding a new partner requires only attaching their account to the existing Share, minimizing administrative overhead. Option B creates three separate Shares with full copies, which multiplies storage costs and administrative effort for each new partner — it also relies on partners to self-enforce data restrictions, which violates isolation requirements. Option C misuses the Marketplace (designed for broad distribution, not controlled partner sharing) and relies on a post-install script that is insecure and operationally fragile. Option D uses Reader Accounts, which are designed for external parties without existing Snowflake accounts — since all partners already have Snowflake accounts, Reader Accounts add unnecessary management overhead and are not the right fit.

Option B is correct: This requires two complementary features. Snowflake Budgets provide object-level spending limits with configurable alert thresholds (e.g., alert at 80%) and are specifically designed for cost governance at a granular level. However, Budgets primarily provide visibility and alerts — for hard enforcement (automatic suspension), warehouse-level Resource Monitors with SUSPEND or SUSPEND_IMMEDIATE actions at the credit equivalent of $5,000 are needed. Using both together achieves the full requirement: proactive alerts at 80% via Budgets and hard enforcement at 100% via Resource Monitors. Option A is incorrect because a single account-level Resource Monitor cannot enforce per-business-unit limits — it aggregates all warehouse spend, so one business unit overspending would penalize others; the thresholds on a single monitor also cannot be split per-BU. Option C is incorrect because Snowflake does not have native 'cost allocation tags' in the same sense as AWS/Azure; while query tags exist for attribution, enforcement via a third-party tool is not a Snowflake-native solution and does not meet the requirement for automated enforcement. Option D is incorrect because MAX_CLUSTER_COUNT controls concurrency scaling (number of parallel warehouse clusters) in multi-cluster mode, not total credit spend; it cannot translate directly to a dollar cap.