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NEW QUESTION # 177
From what stage can a Snowflake user omit the FROM clause while loading data into a table?
Answer: D
NEW QUESTION # 178
Which command can be used to load data into an internal stage?
Answer: A
Explanation:
The PUT command is used to load data into an internal stage in Snowflake. This command uploads data files from a local file system to a named internal stage, making the data available for subsequent loading into a Snowflake table using the COPY INTO command.
References:
* [COF-C02] SnowPro Core Certification Exam Study Guide
* Snowflake Documentation on Data Loading
NEW QUESTION # 179
A Snowflake user wants to optimize performance for a query that queries only a small number of rows in a table. The rows require significant processing. The data in the table does not change frequently.
What should the user do?
Answer: B
Explanation:
In a scenario where a Snowflake user queries only a small number of rows that require significant processing and the data in the table does not change frequently, the most effective way to optimize performance is by creating a materialized view based on the query. Materialized views store the result of the query and can significantly reduce the computation time for queries that are executed frequently over unchanged data.
Why Materialized Views: Materialized views precompute and store the result of the query. This is especially beneficial for queries that require heavy processing. Since the data does not change frequently, the materialized view will not need to be refreshed often, making it an ideal solution for this use case.
Implementation Steps:
To create a materialized view, use the following SQL command:
CREATE MATERIALIZED VIEW my_materialized_view AS SELECT ... FROM my_table WHERE ...; When the query is run, Snowflake uses the precomputed results from the materialized view, thus skipping the need for recalculating the data and improving query performance.
NEW QUESTION # 180
How can a Snowflake user post-process the result of SHOW FILE FORMATS?
Answer: C
Explanation:
first run SHOW FILE FORMATS
then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))
https://docs.snowflake.com/en/sql-reference/functions/result_scan#usage-notes
NEW QUESTION # 181
When floating-point number columns are unloaded to CSV or JSON files, Snowflake truncates the values to approximately what?
Answer: A
Explanation:
When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.
Reference:
Snowflake Documentation: Data Unloading Considerations
NEW QUESTION # 182
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