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A table, EMP_ TBL has three records as shown:
The following variables are set for the session:
Which SELECT statements will retrieve all three records? (Select TWO).
The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.
The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.
The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.
Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.
Option C is incorrect because it uses identifier<Stbl_ref>, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.
Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead.Reference:
Snowflake Documentation: Identifier Function
Snowflake Documentation: Session Variables
Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide
How does a standard virtual warehouse policy work in Snowflake?
A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.
Snowflake Documentation: Multi-cluster Warehouses
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)
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
);
1: CREATE PIPE | Snowflake Documentation
2: Transforming Data During a Load | Snowflake Documentation
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake's micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector.Reference:
Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)
According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.
Limitations on Creating Materialized Views | Snowflake Documentation
