COF-C02–100% Free Exam Collection | Professional 100% SnowPro Core Certification Exam Accuracy
COF-C02–100% Free Exam Collection | Professional 100% SnowPro Core Certification Exam Accuracy
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Tags: Exam COF-C02 Collection, 100% COF-C02 Accuracy, Latest COF-C02 Demo, COF-C02 Labs, New COF-C02 Dumps Ebook
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Snowflake COF-C02 Certification Exam is an industry-recognized credential that showcases the candidate's proficiency in cloud data warehousing. SnowPro Core Certification Exam certification program is ideal for professionals who are looking to enhance their skills in cloud data management and analytics. SnowPro Core Certification Exam certification provides a competitive edge to the candidates over others in the industry and increases their chances of getting hired by top organizations.
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Snowflake SnowPro Core Certification Exam Sample Questions (Q107-Q112):
NEW QUESTION # 107
Which categories are included in the execution time summary in a Query Profile? (Select TWO).
- A. Spilling
- B. Pruning
- C. Local Disk I/O
- D. Initialization
- E. Percentage of data read from cache
Answer: B,D
Explanation:
In the execution time summary of a Query Profile in Snowflake, the categories included provide insights into various aspects of query execution. "Pruning" refers to the process by which Snowflake reduces the amount of data scanned by eliminating partitions of data that are not relevant to the query, thus improving performance. "Initialization" represents the time taken for query planning and setup before actual execution begins. These metrics are crucial for understanding and optimizing query performance.
NEW QUESTION # 108
When should a multi-cluster virtual warehouse be used in Snowflake?
- A. When there are no concurrent queries running on the warehouse
- B. When queuing is delaying query execution on the warehouse
- C. When there is significant disk spilling shown on the Query Profile
- D. When dynamic vertical scaling is being used in the warehouse
Answer: B
Explanation:
A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:
A . When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.
This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.
Reference:
Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation
NEW QUESTION # 109
Which table function is used to perform additional processing on the results of a previously-run query?
- A. QUERY HISTORY BY SESSION
- B. DESCRIBE_RESULTS
- C. QUERY_HISTORY
- D. RESULT_SCAN
Answer: D
Explanation:
The RESULT_SCAN table function is used in Snowflake to perform additional processing on the results of a previously-run query. It allows users to reference the result set of a previous query by its query ID, enabling further analysis or transformations without re-executing the original query.
Reference:
Snowflake Documentation: RESULT_SCAN
NEW QUESTION # 110
What type of query benefits the MOST from search optimization?
- A. A query that uses equality predicates or predicates that use IN
- B. A query that filters on semi-structured data types
- C. A query that includes analytical expressions
- D. A query that uses only disjunction (i.e., OR) predicates
Answer: A
NEW QUESTION # 111
By default, how long is the standard retention period for Time Travel across all Snowflake accounts?
- A. 7 days
- B. 0 days
- C. 1 day
- D. 14 days
Answer: C
Explanation:
By default, the standard retention period for Time Travel in Snowflake is 1 day across all Snowflake accounts. Time Travel enables users to access historical data within this retention window, allowing for point-in-time data analysis and recovery. This feature is a significant aspect of Snowflake's data management capabilities, offering flexibility in handling data changes and accidental deletions.
Reference:
Snowflake Documentation: Using Time Travel
NEW QUESTION # 112
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