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| Section | Weight | Objectives |
|---|---|---|
| Data Protection & Data Sharing | 10% | - Data recovery & retention
|
| Account Access & Security | 20% | - Data protection & governance
|
| Data Pipelines & Automation | 10% | - Workflow & automation
|
| Performance Concepts & Optimization | 15% | - Data optimization features
|
| Data Loading & Transformation | 20% | - Data transformation & querying
|
| Snowflake Architecture & Cloud Concepts | 25% | - Cloud data platform fundamentals
|
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NEW QUESTION # 687
What does a Query Profile metric that shows excessive spillage indicate?
Answer: A
Explanation:
* Excessive spillage in the Query Profile indicates that intermediate query results are being written to remote storage (disk) instead of being processed in memory. This usually happens when:
* The virtual warehouse does not have sufficient resources (e.g., memory, compute power).
* Large datasets or complex operations (e.g., joins, aggregations) require more memory than available.
Solution:
* Increase the size of the virtual warehouse to provide more compute and memory resources.
Why Other Options Are Incorrect:
* A. Poor query optimization: This could contribute, but spillage is primarily tied to resource constraints.
* B. Expired temporary tables: Temporary tables have no relation to spillage.
* D. Improper CTEs: The use of CTEs does not directly cause spillage.
References:
* Query Profile and Performance
NEW QUESTION # 688
What are valid sub-clauses to the OVER clause for a window function? (Select TWO).
Answer: C,E
Explanation:
Valid sub-clauses to theOVERclause for a window function in SQL are:
C). ORDER BY: This clause specifies the order in which the rows in a partition are processed by the window function. It is essential for functions that depend on the row order, such as ranking functions.
D). PARTITION BY: This clause divides the result set into partitions to which the window function is applied.
Each partition is processed independently of other partitions, making it crucial for functions that compute values across sets of rows that share common characteristics.
These clauses are fundamental to defining the scope and order of data over which the window function operates, enabling complex analytical computations within SQL queries.
References:
Snowflake Documentation: Window Functions
NEW QUESTION # 689
The following settings are configured:
THE MIN_DATA_RETENTION_TIME_IN_DAYS is set to 5 at the account level.
THE DATA_RETENTION_TIME_IN_DAYS is set to 2 at the object level.
For how many days will the data be retained at the object level?
Answer: B
Explanation:
The settings shown in the image indicate that the data retention time in days is configured at two different levels: the account level and the object level. At the account level, the MIN_DATA_RETENTION_TIME_IN_DAYS is set to 5 days, and at the object level, the DATA_RETENTION_TIME_IN_DAYS is set to 2 days. Since the object level setting has a lower value, it takes precedence over the account level setting for the specific object. Therefore, the data will be retained for 2 days at the object level.
Reference: Snowflake Documentation on Data Retention Policies
NEW QUESTION # 690
What does Snowflake recommend as a best practice for using secure views?
Answer: A
Explanation:
Snowflake recommends not exposing sequence-generated columns in secure views. Secure views are used to protect sensitive data by ensuring that users can only access data for which they have permissions. Exposing sequence-generated columns can potentially reveal information about the underlying data structure or the number of rows, which might be sensitive.
* Create Secure Views:Define secure views using theSECUREkeyword to ensure they comply with Snowflake's security policies.
* Exclude Sensitive Columns:When creating secure views, exclude columns that might expose sensitive information, such as sequence-generated columns.
CREATE SECURE VIEW secure_view AS
SELECT col1, col2
FROM sensitive_table
WHERE sensitive_column IS NOT NULL;
References:
* Snowflake Documentation: Secure Views
* Snowflake Documentation: Creating Secure Views
These answers and explanations should provide comprehensive guidance on the specified Snowflake topics.
NEW QUESTION # 691
Which function can be used to convert semi-structured data into rows and columns?
Answer: A
Explanation:
To convert semi-structured data into rows and columns in Snowflake, the FLATTEN function is utilized.
FLATTEN Function: This function takes semi-structured data (e.g., JSON) and transforms it into a relational table format by breaking down nested structures into individual rows. This process is essential for querying and analyzing semi-structured data using standard SQL operations.
Example Usage:
SELECT
f.value:attribute1 AS attribute1,
f.value:attribute2 AS attribute2
FROM
my_table,
LATERAL FLATTEN(input => my_table.semi_structured_column) f;
Reference:
Snowflake Documentation on FLATTEN
NEW QUESTION # 692
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