In the Query Profile, what does the Pruning section provide?
Answer(s): D
The Pruning section of the Snowsight Query Profile showshow Snowflake eliminated unnecessary micro-partitions from the scan phaseof the query. Snowflake stores data in micro-partitions and maintains metadata such as min/max values for each column within each partition. When a query includes filters (e.g., WHERE clauses), Snowflake evaluates this metadata to determine which micro- partitions cannot possibly satisfy the predicate. These partitions are skipped, meaning they are never scanned or read from storage.This process drastically improves performance because Snowflake minimizes I/O, reduces compute usage, and shortens execution time. Partition pruning is especially impactful on large tables because only a fraction of the stored micro-partitions typically need to be accessed.The Pruning section does not show removedrows--that happens during the filter step. It does not show removedcolumns--column pruning is handled separately by the optimizer. It also does not show removedobjectsfrom the plan. Its sole purpose is to document micro-partition elimination and scan reduction.
What are the key benefits of the Snowflake multi-cluster shared data architecture? (Select TWO).
Answer(s): C,E
The Snowflake multi-cluster shared data architecture separates compute and storage, enablingindependent scaling of each. Compute resources (virtual warehouses) operate independently from the centralized storage layer, allowing users to increase compute power without impacting storage costs--or vice versa. This flexibility provides major advantages for cost optimization and workload performance tuning.The architecture also supportsnear-unlimited concurrency and elasticity. When many users or workloads run simultaneously, Snowflake can automatically add additional clusters to a multi-cluster warehouse, ensuring that no queries experience queuing or performance degradation. This capability is crucial for BI dashboards, ETL pipelines, and large organizations with varied usage patterns.Columnar storage (option D) is indeed a Snowflake feature but is not unique to the multi-cluster architecture. Enhanced security arrives via Snowflake's platform-wide mechanisms, not specifically because of multi-cluster architecture. Unstructured data loading (option B) is also not governed by multi-cluster behavior.
How can Snowsight be used to monitor the performance of a virtual warehouse?
To monitor virtual warehouse performance in Snowsight, the correct approach is using theQUERY_HISTORYview or its Snowsight equivalent under the Activity section. QUERY_HISTORY provides detailed metrics on execution time, queueing, credit consumption, compilation time, and warehouse utilization for every executed query. By analyzing this information, users can diagnose issues such as concurrency bottlenecks, under-sized warehouses, long-running queries, or excessive I/O.SHOW WAREHOUSES returns metadata about warehouse configurations (size, state, scaling policy) but does not provide performance data. LOAD_HISTORY tracks COPY INTO operations only and is not representative of general warehouse workload. DESCRIBE WAREHOUSE shows configuration details but no runtime or historical performance metrics.Thus, the most accurate and complete way to assess warehouse performance--particularly execution delays, queuing, or excessive credit usage--is by using QUERY_HISTORY.
Which column is returned when the FLATTEN table function is executed?
Answer(s): A
The FLATTEN table function expands semi-structured data such as arrays or objects stored in VARIANT columns. Its output includes several metadata columns, and the most important among them isVALUE, which contains the extracted element from the array or object. VALUE is the column that developers typically join on or reference to access the flattened data.In addition to VALUE, FLATTEN returns KEY (for object keys), INDEX (position in arrays), SEQ (internal ordering), and PATH (hierarchical reference path). These other columns support analytical or transformation logic, but VALUE remains the primary output used for downstream SQL.The other listed options--OUTPUT, ROWNUM, and LEVEL--are not part of the FLATTEN function's output schema. ROWNUM and LEVEL may appear in other SQL contexts but not in FLATTEN results.Therefore, the correct answer is VALUE.
What are virtual warehouses in Snowflake?
A virtual warehouse in Snowflake is acompute resourcethat supplies CPU, memory, and temporary storage used to process SQL queries, perform data loading, execute DML operations, and run procedural logic. Warehouses are elastic, can be resized on demand, suspended to save costs, and automatically resumed when needed.Virtual warehouses donotstore data--data is kept in Snowflake's centralized storage layer. They also are not user accounts; user accounts belong to Snowflake's identity management system. Worksheets are UI components in Snowsight or Classic Console used to write and run SQL, not compute resources.Thus, virtual warehouses are Snowflake's compute engine, enabling all processing without managing physical infrastructure.
What types of worksheets can be created in Snowsight? (Select TWO).
Answer(s): A,E
Snowsight supports two worksheet types:SQL worksheetsandPython worksheets. SQL worksheets allow users to execute queries, create objects, and perform data analysis using ANSI SQL and Snowflake-specific extensions. Python worksheets, powered by Snowpark, allow users to write Python code that interacts directly with Snowflake tables, data frames, and machine learning workflows.Java, Scala, and JavaScript are supported via Snowpark APIs or UDF development, but they cannot be used as worksheet languages. Worksheets are designed for interactive analysis, visualization, and iterative development, with native runtimes only for SQL and Python.Thus, only SQL and Python worksheets can be created within Snowsight.
What is the default Time Travel data retention period?
Answer(s): B
The default Time Travel retention period for most objects in Snowflake is1 day(24 hours). Time Travel enables access to historical versions of data after updates, deletes, or drops. It also allows cloning databases, schemas, and tables at previous points in time, and recovering dropped objects.While Snowflake Enterprise Edition and higher tiers allow retention periods up to 90 days, this extended window is not the default--administrators must explicitly configure it for each table, schema, or database.Retention periods of 7, 45, or 90 days are possible only with higher service editions; the default for all accounts and objects remains 1 day unless explicitly overridden.
When loading data into a table using the COPY INTO command, what is the default error-handling setting?
By default, Snowflake usesON_ERROR = ABORT_STATEMENTfor COPY INTO. This means that if a single row fails to load due to issues such as type mismatches or malformed data, Snowflake immediately stops the entire load operation. This behavior ensures data integrity by preventing partial or incomplete loads when errors occur.Other error-handling modes must be explicitly specified: CONTINUE skips erroneous rows, SKIP_FILE skips whole files with errors, and VALIDATE is part of the validation mode feature, not a default COPY behavior.Thus, unless explicitly changed, COPY INTO always uses ABORT_STATEMENT.
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