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SnowPro-Advanced-Data-Engineer SnowPro Advanced Data Engineer

  • Snowflake
  • 5 free sample questions

About this exam

SnowPro-Advanced-Data-Engineer is the Snowflake exam SnowPro Advanced Data Engineer. Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
SnowPro-Advanced-Data-Engineer
Vendor
Snowflake
Practice questions
Not available yet
Free sample questions
5
Question bank last updated
Not available yet

Try 2 sample questions

Taken from the free sample set for SnowPro-Advanced-Data-Engineer.

Question 1 (choose 2)

For using Snowflake with Spark, which of the below privileges are required?

  1. Option A:

    USAGE on the schema that contains the table that you will read from or write toA.

  2. Option B:

    CREATE STAGE on the schema that contains the table that you will read from or write toB.

  3. Option C:

    AccountadminC.

  4. Option D:

    Sysadmin

Show answer and explanation

Correct answer: A, B

https://docs.snowflake.com/en/user-guide/spark-connector-install.html#requirements Requirements To install and use Snowflake with Spark, you need the following: A supported operating system. For a list of supported operating systems, see Operating System Support. Snowflake Connector for Spark. Snowflake JDBC Driver (the version compatible with the version of the connector) Apache Spark environment, either self-hosted or hosted in any of the following: Qubole Data Service. Databricks. Amazon EMR. In addition, you can use a dedicated Amazon S3 bucket or Azure Blob storage container as a staging zone between the two systems; however, this is not required with version 2.2.0 (and higher) of the connector, which uses a temporary Snowflake internal stage (by default) for all data exchange. The role used in the connection needs USAGE and CREATE STAGE privileges on the schema that contains the table that you will read from or write to

Question 2

The employee project details has the project names as array against each employee as shown below


  1. Option A:

    Which of the query below will convert the array into individual rows? select emp_id, emp_name, p.value::string as project_names from employee_project_details,table(flatten(employee_project_details.project_names)) p ;

  2. Option B:

    select emp_id, emp_name, p.value::string as project_names from employee_project_details,lateral(flatten(employee_project_details.project_names)) p

  3. Option C:

    select emp_id, emp_name, p.value::string as project_names from employee_project_details,lateral(flatten(employee_project_details.project_names)) p ;

  4. Option D:

    select emp_id, emp_name, p.value::string as project_names from employee_project_details,lateral flatten(employee_project_details.project_names)) p

Show answer and explanation

Correct answer: A

Try this out in your snowflake instance Step 1 - Create the table create or replace table employee_project_details(emp_id varchar, emp_name varchar, project_names array); Step 2 - Insert values insert into employee_project_details select '1','john',array_cat(to_array('it'),to_array('prod')); Step 3 - Convert to rows select emp_id, emp_name, p.value::string as project_names from employee_project_details,table(flatten(employee_project_details.project_names)) p

See all 5 free sample questions