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  1. Home
  2. Snowflake Certification
  3. COF-C02 Exam
  4. Snowflake.COF-C02.v2024-11-28.q521 Dumps
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Question 86

Which of the following is an example of an operation that can be completed without requiring compute, assuming no queries have been executed previously?

Correct Answer: B
Operations that do not require compute resources are typically those that can leverage previously cached results. However, if no queries have been executed previously, all the given operations would require compute to execute. It's important to note that certain operations like DDL statements and queries that hit the result cache do not consume compute credits2.
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Question 87

A Snowflake user executed a query and received the results. Another user executed the same query 4 hours later. The data had not changed.
What will occur?

Correct Answer: D
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Question 88

The effects of query pruning can be observed by evaluating which statistics? (Select TWO).

Correct Answer: A,C
Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:
* Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.
* Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.
Options B, D, and E do not directly relate to observing the effects of query pruning. "Partitions total" shows the total available, not the impact of pruning, while "Bytes read from result" and "Bytes written" relate to output rather than the efficiency of data scanning.References: Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.
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Question 89

Which Snowflake architectural layer is responsible for a query execution plan?

Correct Answer: C
In Snowflake's architecture, the Cloud Services layer is responsible for generating the query execution plan.
This layer handles all the coordination, optimization, and management tasks, including query parsing, optimization, and compilation into an execution plan that can be processed by the Compute layer.
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Question 90

How does the Access_History view enhance overall data governance pertaining to read and write operations?
(Select TWO).

Correct Answer: B,E
The ACCESS_HISTORY view in Snowflake is a powerful tool for enhancing data governance, especially concerning monitoring and auditing data access patterns for both read and write operations. The key ways in which ACCESS_HISTORY enhances overall data governance are:
* B. Provides a unified picture of what data was accessed and when it was accessed: This view logs details about query executions, including the objects (tables, views) accessed and the timestamps of these accesses. It's instrumental in auditing and compliance scenarios, where understanding the access patterns to sensitive data is critical.
* E. Determines whether a given row in a table can be accessed by the user by filtering the data based on a given policy: While this option is a bit of a misinterpretation of what ACCESS_HISTORY directly offers, it indirectly supports data governance by providing the information necessary to analyze access patterns. This analysis can then inform policy decisions, such as implementing Row-Level Security (RLS) to restrict access to specific rows based on user roles or attributes.
ACCESS_HISTORY does not automatically apply data masking or tag columns with personal information.
However, the insights derived from analyzing ACCESS_HISTORY can be used to identify sensitive data and inform the application of masking policies or other security measures.
References:
* Snowflake Documentation on ACCESS_HISTORY: Access History
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