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| Section | Objectives |
|---|---|
| Data Transformations and Querying | - SQL querying in Snowflake - Semi-structured data handling (VARIANT, JSON, Parquet) |
| Snowflake Architecture and Overview | - Virtual warehouses and resource scaling concepts - Snowflake architecture concepts (cloud data platform, separation of storage and compute) |
| Data Loading and Unloading | - Data unloading and external stages - Bulk loading using stages and COPY INTO |
| Data Protection, Security, and Performance | - Role-based access control (RBAC) - Query performance optimization basics - Time Travel and Fail-safe concepts |
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NEW QUESTION # 890
Which JSON paths are considered to be equivalent in Snowflake? (Choose two.)
Answer: C,D
NEW QUESTION # 891
True or False: When a new Snowflake object is created, it is automatically owned by the user who created it.
Answer: B
NEW QUESTION # 892
While running a query on a virtual warehouse in auto-scale mode, additional clusters are stated immediately if which setting is configured?
Answer: C
Explanation:
In Snowflake, auto-scaling allows virtual warehouses to automatically start additional clusters to handle increasing query loads. The setting that triggers the immediate startof additional clusters when a warehouse is running in auto-scale mode is:A. MAX_CLUSTER_COUNT is increased and new_max_clusters is greater than running_clusters: When the maximum number of clusters (MAX_CLUSTER_COUNT) is increased and the new maximum is higher than the number of clusters currently running (running_clusters), additional clusters will start immediately if required by the workload. This configuration ensures that performance scales with demand by allowing more compute resources to be provisioned as needed.
This behavior is designed to maintain performance by dynamically adjusting the compute resources without manual intervention, ensuring that queries are executed with minimal delay, even under varying workloads. It aligns with the principles of elasticity and scalability in cloud computing, particularly within Snowflake's architecture.
NEW QUESTION # 893
Which function will convert semi-structured data to a relational data representation'?
Answer: D
Explanation:
* The FLATTEN function converts semi-structured data (e.g., JSON or XML) into a relational representation by expanding nested structures into rows.
* Example:
SELECT FLATTEN(INPUT => PARSE_JSON('[{"key": "value1"}, {"key": "value2"}]')); References:
* Snowflake Documentation: FLATTEN Function
* Snowflake Semi-Structured Data Handling
NEW QUESTION # 894
Which of the following describes external functions in Snowflake?
Answer: A
NEW QUESTION # 895
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