Snowflake Updated ADA-C02 Exam Questions and Answers by jonathan

A Snowflake multi-cluster warehouse is appropriate when workloads generate consistent query concurrency with occasional increases in demand.

The scenario indicates that two Small clusters are required for routine activity, with temporary increases to three or four clusters during busier periods.

Option B correctly establishes a baseline of two clusters and allows Snowflake to automatically increase capacity when concurrent query demand grows.

When query activity declines, additional clusters can be removed according to the warehouse's scaling policy. AUTO_SUSPEND and AUTO_RESUME provide additional controls to prevent unnecessary compute usage during periods of inactivity.

The ANALYST role requires only USAGE to execute queries, preserving least-privilege access without permitting warehouse configuration changes.

Option A is unsuitable because manual cluster management introduces unnecessary administrative responsibility. Moreover, MANAGE is not the applicable object-level privilege for the described warehouse operations.

Option C confuses warehouse size with independent cluster capacity. A single X-Large warehouse does not provide the same concurrency-scaling behavior as four independently managed Small clusters.

Option D introduces operational complexity and requires users to select warehouses manually.

Terminology warning: The original choices use MIN_CLUSTERS. The actual Snowflake configuration parameter is MIN_CLUSTER_COUNT. Option B is conceptually correct, but its wording should be corrected before publication.

Official Snowflake references: Multi-cluster Warehouses; CREATE WAREHOUSE; Access Control Privileges.

Snowflake distinguishes between cluster-level scaling and warehouse-level automatic suspension.

A multi-cluster warehouse operating in auto-scale mode can start additional clusters when query concurrency increases. As demand declines, Snowflake removes excess clusters according to the configured scaling policy until the warehouse returns to its minimum cluster count.

Automatic suspension operates at the warehouse level rather than independently suspending each cluster.

Snowflake explicitly specifies that automatic suspension occurs when the minimum number of clusters is running and the warehouse has remained inactive for the configured AUTO_SUSPEND interval.

For example, consider a warehouse configured with MIN_CLUSTER_COUNT=2, MAX_CLUSTER_COUNT=8, and AUTO_SUSPEND=300.

After a workload peak, Snowflake progressively scales down additional clusters. Once the warehouse has returned to two clusters and satisfies the inactivity condition, automatic suspension can occur.

Option A is incomplete because it does not account for the minimum-cluster requirement in Snowflake's documented suspension process.

Option B incorrectly associates automatic suspension with reaching the maximum cluster count.

Option D is incorrect because multi-cluster warehouses fully support automatic suspension.

Administrators should remember that Snowflake evaluates suspension periodically, so the configured interval is not an exact real-time shutdown guarantee.

Official Snowflake references: Overview of Warehouses: Auto-suspension and Auto-resumption; Multi-cluster Warehouses.

Snowflake external tables provide a relational interface for querying structured and semi-structured data files stored outside Snowflake, typically in cloud object storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage.

An external table references files through an external stage. Snowflake maintains file-level metadata and exposes the external data through a table-like abstraction, allowing SQL queries without requiring an initial COPY INTO operation to populate an internal Snowflake table.

This simplifies data pipelines when source files must remain in cloud storage, particularly for data lakes, historical archives, and externally managed datasets.

Option A describes accessing Snowflake from another relational database, which is not the purpose of an external table.

Option B similarly concerns external database connectivity rather than querying files stored in an external stage.

Option C describes exporting data from Snowflake to external storage, a process generally implemented through unloading operations such as COPY INTO a storage location.

Option D correctly identifies the external-table use case.

External tables are read-only and do not support conventional INSERT, UPDATE, or DELETE operations. Query performance may also be lower than native Snowflake tables because the underlying data remains externally stored.

Official Snowflake references: Introduction to External Tables; Creating External Tables; Querying External Tables.