COF-C03 Best Vce & Exam COF-C03 Vce Format

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Snowflake SnowPro® Core Certification (COF-C03) Sample Questions (Q163-Q168):

NEW QUESTION # 163
If a Snowflake user decides a table should be clustered, what should be used as the cluster key?

Answer: D

Explanation:
When deciding on a clustering key for a table, Snowflake recommends using the columns that are most actively used in the select filters.This is because clustering by these columns can improve the performance of queries that filter on these values, leading to more efficient scans and better overall query performance2.References:[COF-C02] SnowPro Core Certification Exam Study Guide


NEW QUESTION # 164
When using the ALLOW CLIENT_MFA_CACHING parameter, how long is a cached Multi-Factor Authentication (MFA) token valid for?

Answer: B

Explanation:
When using the ALLOW_CLIENT_MFA_CACHING parameter, a cached Multi-Factor Authentication (MFA) token is valid for up to 4 hours.This allows for continuous, secure connectivity without users needing to respond to an MFA prompt at the start of each connection attempt to Snowflake within this timeframe2.


NEW QUESTION # 165
What tasks can be completed using the copy command? (Select TWO)

Answer: C,D

Explanation:
The COPY command in Snowflake allows for the reordering of columns as they are loaded into a table, and it also permits the omission of columns from the source file during the load process. This provides flexibility in handling the schema of the data being ingested. References: [COF-C02] SnowPro Core Certification Exam Study Guide


NEW QUESTION # 166
How can a user get the MOST detailed information about individual table storage details in Snowflake?

Answer: A

Explanation:
To get the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view should be used. This Information Schema view provides granular storage metrics for tables within Snowflake, including data related to the size of the table, the amount of data stored, and storage usage over time. It's an essential tool for administrators and users looking to monitor and optimize storage consumption and costs.
References:
Snowflake Documentation: Information Schema - TABLE STORAGE METRICS View


NEW QUESTION # 167
A query containing a WHERE clause is running longer than expected. The Query Profile shows that all micro-partitions being scanned How should this query be optimized?

Answer: B

Explanation:
When a query containing a WHERE clause is running longer than expected, and the Query Profile shows that all micro-partitions are being scanned, the query can be optimized by adding a clustering key to the table.
Understanding Micro-Partitioning in Snowflake:
Snowflake automatically partitions tables into micro-partitions for efficient storage and query performance.
Each micro-partition contains metadata about the range of values it holds, which helps in pruning irrelevant partitions during query execution.
Role of Clustering Keys:
A clustering key defines how data in a table is organized within micro-partitions.
By specifying a clustering key, you can control the physical layout of data, ensuring that related rows are stored together.
This organization improves query performance by reducing the number of micro-partitions that need to be scanned.
Optimizing Queries with Clustering Keys:
Adding a clustering key based on columns frequently used in WHERE clauses helps Snowflake quickly locate and scan relevant micro-partitions.
This minimizes the amount of data scanned and reduces query execution time.
Example:
ALTER TABLE my_table CLUSTER BY (column1, column2);
This command adds a clustering key to my_table using column1 and column2.
Future queries that filter on these columns will benefit from improved performance.
Benefits:
Reduced query execution time: Fewer micro-partitions need to be scanned.
Improved resource utilization: More efficient data retrieval leads to lower compute costs.
References:
Snowflake Documentation: Clustering Keys
Snowflake Documentation: Query Profile


NEW QUESTION # 168
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