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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Handling and Transformation | 15-20% | - Data Transformation
|
| Topic 2: Billing and Account Management | 10-15% | - Credit Usage
|
| Topic 3: Cloud Architecture and Data Loading | 20-25% | - Snowflake Architecture Overview
|
| Topic 4: Performance Optimization | 20-25% | - Data Optimization
|
| Topic 5: Account Access and Security | 20-25% | - Authorization
|
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NEW QUESTION # 253
At what level is the MIN_DATA_RETENSION_TIME_IN_DAYS parameter set?
Answer: A
Explanation:
TheMIN_DATA_RETENTION_TIME_IN_DAYSparameter is set at theAccountlevel in Snowflake. This parameter specifies the minimum number of days Snowflake retains the historical data for time travel, which allows users to access and query data as it existed at previous points in time.
Here's how to understand and adjust this parameter:
Purpose of MIN_DATA_RETENTION_TIME_IN_DAYS:This parameter is crucial for managing data lifecycle and compliance requirements within Snowflake. It determines the minimum time frame for which you can perform operations like restoring deleted objects or accessing historical versions of data.
Setting the Parameter:Only account administrators can set or modify this parameter. It is done at the account level, impacting all databases and schemas within the account. The setting can be adjusted based on the organization's data retention policy.
Adjusting the Parameter:
To view the current setting, use:
SHOWPARAMETERSLIKE'MIN_DATA_RETENTION_TIME_IN_DAYS';
To change the setting, an account administrator can execute:
ALTERACCOUNTSETMIN_DATA_RETENTION_TIME_IN_DAYS = <number_of_days>;
Reference:For more details on data retention and time travel in Snowflake, refer to the official documentation:
https://docs.snowflake.com/en/sql-reference/account-usage/storage-usage.html#data-retention-time-travel
NEW QUESTION # 254
A user has semi-structured data to load into Snowflake but is not sure what types of operations will need to be performed on the data. Based on this situation, what type of column does Snowflake recommend be used?
Answer: D
Explanation:
When dealing with semi-structured data in Snowflake, and the specific types of operations to be performed on the data are not yet determined, Snowflake recommends using the VARIANT data type. The VARIANT type is highly flexible and capable of storing data in multiple formats, including JSON, AVRO, BSON, and more, within a single column. This flexibility allows users to perform various operations on the data, including querying and manipulation of nested data structures without predefined schemas.
References:
* Snowflake Documentation: Semi-structured Data Types
NEW QUESTION # 255
Which Snowflake URL type is used by directory tables?
Answer: B
NEW QUESTION # 256
Which commands can only be executed using SnowSQL? (Select TWO).
Answer: D,E
Explanation:
The LIST and PUT commands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients. LIST is used to display the contents of a stage, and PUT is used to upload files to a stage. Reference: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 257
Which commands extract the value for a key, key_1, from an object column? Select TWO.
Answer: B,E
Explanation:
The correct answers are A. SELECT column:key_1 FROM my_table; and C. SELECT column[ ' key_1 '
] FROM my_table; .
Snowflake supports path notation for querying semi-structured data stored in columns such as VARIANT, OBJECT, and ARRAY. For a JSON object key, Snowflake supports colon path notation and bracket notation.
Why A is correct:
The colon path notation accesses a field inside a semi-structured column.
Example:
SELECT column:key_1
FROM my_table;
This extracts the value associated with the key key_1.
Why C is correct:
Bracket notation also accesses an object element by key.
Example:
SELECT column[ ' key_1 ' ]
FROM my_table;
This is especially useful when key names require quoting or contain special characters.
Why the other options are incorrect:
B). Dot notation is supported for nested traversal after the initial colon path, such as column:key_1.subkey, but column.key_1 by itself is interpreted as regular SQL object/column qualification, not as extracting a key from a semi-structured column.
D). Parentheses are not valid JSON object key extraction syntax in Snowflake.
E). Casting a column to string and aliasing it does not extract the value for the key key_1.
Official Snowflake documentation reference:
Snowflake documentation explains that elements in semi-structured data can be accessed using colon path notation and bracket notation. Dot notation may also be used for nested paths after the semi-structured column reference.
Reference: Snowflake Documentation - Querying semi-structured data; Snowflake Documentation - Traversing semi-structured data; SnowPro Core Study Guide - Working with Semi-Structured Data.
NEW QUESTION # 258
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