Snowflake SnowPro Advanced Data Engineer Exam Practice Questions (P. 3)
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Question #11
A new CUSTOMER table is created by a data pipeline in a Snowflake schema where MANAGED ACCESS is enabled.
Which roles can grant access to the CUSTOMER table? (Choose three.)
Which roles can grant access to the CUSTOMER table? (Choose three.)
- AThe role that owns the schemaMost Voted
- BThe role that owns the database
- CThe role that owns the CUSTOMER table
- DThe SYSADMIN role
- EThe SECURITYADMIN roleMost Voted
- FThe USERADMIN role with the MANAGE GRANTS privilegeMost Voted
Correct Answer:
ABC
ABC

In a Snowflake environment where a schema has MANAGED ACCESS enabled, only specific roles can grant access to the tables within that schema. The role owning the schema definitely has this ability because they directly control all underlying objects by virtue of ownership. Similarly, the database owner oversees everything at the database level, including all schemas and therefore can also manage access. The direct owner of the table, i.e., the role that created or has been assigned ownership of the CUSTOMER table, naturally retains permission to manage its access. Other roles like SYSADMIN, SECURITYADMIN, or USERADMIN do not inherently possess the ability to manage access solely based on their title unless explicitly granted relevant privileges. Remember that each role must be explicitly equipped with the necessary privileges specific to the access control requirements of a MANAGED ACCESS schema.
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Question #12
What is the purpose of the BUILD_STAGE_FILE_URL function in Snowflake?
- AIt generates an encrypted URL for accessing a file in a stage.
- BIt generates a staged URL for accessing a file in a stage.
- CIt generates a permanent URL for accessing files in a stage.Most Voted
- DIt generates a temporary URL for accessing a file in a stage.
Correct Answer:
C
C

The BUILD_STAGE_FILE_URL function in Snowflake creates a permanent URL which enables enduring access to files stored in a specified stage. This is particularly useful as it allows users and systems to repeatedly access the same file without needing to regenerate access URLs, thereby simplifying integration and access workflows in long-term data handling scenarios.
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Question #13
The JSON below is stored in a VARIANT column named V in a table named jCustRaw:

Which query will return one row per team member (stored in the teamMembers array) along with all of the attributes of each team member?

Which query will return one row per team member (stored in the teamMembers array) along with all of the attributes of each team member?
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Question #14
A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data Engineer needs to move these transformations to Snowpark.
What characteristics of data transformations in Snowpark should be considered to meet this requirement? (Choose two.)
What characteristics of data transformations in Snowpark should be considered to meet this requirement? (Choose two.)
- AIt is possible to join multiple tables using DataFrames.Most Voted
- BSnowpark operations are executed lazily on the server.Most Voted
- CUser-Defined Functions (UDFs) are not pushed down to Snowflake.
- DSnowpark requires a separate cluster outside of Snowflake for computations.
- EColumns in different DataFrames with the same name should be referred to with squared brackets.
Correct Answer:
AB
AB

When transitioning Scala scripts to Snowpark for data transformations, it's crucial to recognize that Snowpark operations are lazily executed; operations are compiled into plans on the server but don't run until an action is called. This allows efficient query optimization. Also, Snowpark fully supports manipulating multiple DataFrames, making tasks like joining tables seamless and practical inside the Snowflake environment itself. This integrates well with the existing Snowflake capabilities, ensuring that no additional computation clusters are required outside of Snowflake.
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Question #15
The following is returned from SYSTEM$CLUSTERING_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?

What does the total_constant_partition_count value indicate about this table?
- AThe table is clustered very well on O_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering.Most Voted
- BThe table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro-partition in the table.
- CThe data in O_ORDERDATE does not change very often, as there are 493 micro-partitions containing rows where that column has not been modified since the row was created.
- DThe data in O_ORDERDATE has a very low cardinality, as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition.
Correct Answer:
A
A
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