SnowPro Advanced Data Engineer Practice Test

SnowPro Advanced Data Engineer Practice Test practice questions, page 3 of 7, with answer review and explanations.

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.)

  1. The role that owns the schema
  2. The role that owns the database
  3. The role that owns the CUSTOMER table
  4. The SYSADMIN role
  5. The SECURITYADMIN role
  6. The USERADMIN role with the MANAGE GRANTS privilege

Question 12

What is the purpose of the BUILD_STAGE_FILE_URL function in Snowflake?

  1. It generates an encrypted URL for accessing a file in a stage.
  2. It generates a staged URL for accessing a file in a stage.
  3. It generates a permanent URL for accessing files in a stage.
  4. It generates a temporary URL for accessing a file in a stage.

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?

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.)

  1. It is possible to join multiple tables using DataFrames.
  2. Snowpark operations are executed lazily on the server.
  3. User-Defined Functions (UDFs) are not pushed down to Snowflake.
  4. Snowpark requires a separate cluster outside of Snowflake for computations.
  5. Columns in different DataFrames with the same name should be referred to with squared brackets.

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?

  1. The table is clustered very well on O_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering.
  2. The 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.
  3. The 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.
  4. The 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.