How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?
Answer : C
This option is the best way to transform relational data into semi-structured data using the least amount of operational overhead. The OBJECT_CONSTRUCT function takes a variable number of key-value pairs as arguments and returns a Snowflake object, which is a variant type that can store JSON data. The function can be used to convert each row of relational data into a JSON object with the column names as keys and the column values as values.
The following is returned from SYSTEMCLUSTERING_INFORMATION () for a table named orders with a date column named O_ORDERDATE:
What does the total_constant_partition_count value indicate about this table?
Answer : B
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.
Assuming a Data Engineer has all appropriate privileges and context which statements would be used to assess whether the User-Defined Function (UDF), MTBATA3ASZ. SALES .REVENUE_BY_REGION, exists and is secure? (Select TWO)
Answer : A, B
The statements that would be used to assess whether the UDF, MTBATA3ASZ. SALES .REVENUE_BY_REGION, exists and is secure are:
SHOW DS2R FUNCTIONS LIKE 'REVEX'^BYJIESION' IN SCHEMA SALES;: This statement will show information about the UDF, including its name, schema, database, arguments, return type, language, and security option. If the UDF does not exist, the statement will return an empty result set.
SELECT IS_SECURE FROM SNOWFLAKE. INFCRXATION_SCKZMA. FUNCTIONS WHERE FUNCTI0N_3CHEMA = 'SALES' AND FUNCTI CN_NAXE = *ftEVEXUE_BY_RKXQH4;: This statement will query the SNOWFLAKE.INFORMATION_SCHEMA.FUNCTIONS view, which contains metadata about the UDFs in the current database. The statement will return the IS_SECURE column, which indicates whether the UDF is secure or not. If the UDF does not exist, the statement will return an empty result set. The other statements are not correct because:
SELECT IS_SEC'JRE FROM INFOR>LVTICN_SCHEMA. FUNCTIONS WHERE FUNCTION_SCHEMA = 'SALES1 AND FUNGTZON_NAME = ' REVENUE_BY_REGION';: This statement will query the INFORMATION_SCHEMA.FUNCTIONS view, which contains metadata about the UDFs in the current schema. However, the statement has a typo in the schema name ('SALES1' instead of 'SALES'), which will cause it to fail or return incorrect results.
SHOW EXTERNAL FUNCTIONS LIKE 'REVENUE_BY_REGION' IB SCHEMA SALES;: This statement will show information about external functions, not UDFs. External functions are Snowflake functions that invoke external services via HTTPS requests and responses. The statement will not return any results for the UDF.
SHOW SECURE FUNCTIONS LIKE 'REVENUE 3Y REGION' IN SCHEMA SALES;: This statement is invalid because there is no such thing as secure functions in Snowflake. Secure functions are a feature of some other databases, such as PostgreSQL, but not Snowflake. The statement will cause a syntax error.
What kind of Snowflake integration is required when defining an external function in Snowflake?
Answer : A
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an external service via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.
The isolation level of Snowflake streams is repeatable read, which means that each transaction sees a consistent snapshot of data that does not change during its execution. Streams use time travel internally to provide this isolation level and ensure that queries on streams return consistent results regardless of concurrent transactions on their source tables.
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