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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Accounts and Security | 25% | - Governance and Compliance
|
| Topic 2: Performance Optimization | 20% | - Query Performance
|
| Topic 3: Snowflake Architecture | 30% | - Platform Architecture Design
|
| Topic 4: Data Engineering | 25% | - Data Transformation
|
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NEW QUESTION # 12
Which of the following are characteristics of Snowflake's parameter hierarchy?
Answer: B
Explanation:
Explanation
This is the correct answer because it reflects the characteristics of Snowflake's parameter hierarchy.
Snowflake provides three types of parameters that can be set for an account: account parameters, session parameters, and object parameters. All parameters have default values, which can be set and then overridden at different levels depending on the parameter type. The following diagram illustrates the hierarchical relationship between the different parameter types and how individual parameters can be overridden at each level1:
As shown in the diagram, schema parameters are a type of object parameters that can be set for schemas.
Schema parameters can override the account parameters that are set at the account level. For example, the LOG_LEVEL parameter can be set at the account level to control the logging level for all objects in the account, but it can also be overridden at the schema level to control the logging level for specific stored procedures and UDFs in that schema2.
The other options listed are not correct because they do not reflect the characteristics of Snowflake's parameter hierarchy. Session parameters do not override virtual warehouse parameters, because virtual warehouse parameters are a type of session parameters that can be set for virtual warehouses. Virtual warehouse parameters do not override user parameters, because user parameters are a type of session parameters that can be set for users. Table parameters do not override virtual warehouse parameters, because table parameters are a type of object parameters that can be set for tables, and object parameters do not affect session parameters1.
References:
* Snowflake Documentation: Parameters
* Snowflake Documentation: Setting Log Level
NEW QUESTION # 13
Which of the following are characteristics of how row access policies can be applied to external tables? (Choose three.)
Answer: A,B,C
Explanation:
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
Reference:
CREATE EXTERNAL TABLE | Snowflake Documentation
ALTER EXTERNAL TABLE | Snowflake Documentation
Understanding Row Access Policies | Snowflake Documentation
Snowflake Data Governance: Row Access Policy Overview
Secure Views | Snowflake Documentation
NEW QUESTION # 14
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)
Answer: B,E,F
Explanation:
* The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO <table> statement used by Snowpipe to load data from an ingestion queue into tables1. The statement uses a subquery in the FROM clause to transform the data from the staged files before loading it into the table2.
* The transformations supported in the subquery are as follows2:
* Data can be filtered by an optional WHERE clause, which specifies a condition that must be satisfied by the rows returned by the subquery. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
create pipe mypipe as
copy into mytable
from (
select * from @mystage
where col1 = 'A' and col2 > 10
);
* Columns can be reordered, which means changing the order of the columns in the subquery to match the order of the columns in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
create pipe mypipe as
copy into mytable (col1, col2, col3)
from (
select col3, col1, col2 from @mystage
);
* Columns can be omitted, which means excluding some columns from the subquery that are not needed in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
create pipe mypipe as
copy into mytable (col1, col2)
from (
select col1, col2 from @mystage
);
* The other options are not supported in the subquery because2:
* Type casts are not supported, which means changing the data type of a column in the subquery.
For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
create pipe mypipe as
copy into mytable (col1, col2)
from (
select col1::date, col2 from @mystage
);
* Incoming data can not be joined with other tables, which means combining the data from the staged files with the data from another table in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
create pipe mypipe as
copy into mytable (col1, col2, col3)
from (
select s.col1, s.col2, t.col3 from @mystage s
join othertable t on s.col1 = t.col1
);
* The ON ERROR - ABORT statement command can not be used, which means aborting the entire load operation if any error occurs. This command can only be used in the COPY INTO <table> statement, not in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
create pipe mypipe as
copy into mytable
from (
select * from @mystage
on error abort
);
References:
* 1: CREATE PIPE | Snowflake Documentation
* 2: Transforming Data During a Load | Snowflake Documentation
NEW QUESTION # 15
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
Answer: B
Explanation:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column- like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi- structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
Considerations for Semi-structured Data Stored in VARIANT
Snowflake Architect Training
Snowflake query performance on unique element in variant column
Snowflake variant performance
NEW QUESTION # 16
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?
Answer: C
NEW QUESTION # 17
......
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