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DEA-C01 SnowPro Advanced Data Engineer Certification Exam

  • Snowflake
  • 5 free sample questions

About this exam

DEA-C01 is the Snowflake exam SnowPro Advanced Data Engineer Certification Exam. Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
DEA-C01
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 DEA-C01.

Question 1

Database XYZ has the data_retention_time_in_days parameter set to 7 days and table xyz.public.ABC has the data_retention_time_in_days set to 10 days.

A Developer accidentally dropped the database containing this single table 8 days ago and just discovered the mistake.

How can the table be recovered?

  1. Option A:

    undrop database xyz;

  2. Option B:

    create -able abc_restore as select * from xyz.public.abc at {offset = > -60*60*24*8};

  3. Option C:

    create table abc_restore clone xyz.public.abc at (offset = > -3€0G*24*3);

  4. Option D:

    Create a Snowflake Support case lo restore the database and tab e from "a i-safe

Show answer and explanation

Correct answer: A

The table can be recovered by using the undrop database xyz; command. This command will restore the database that was dropped within the last 14 days, along with all its schemas and tables, including the customer table. The data_retention_time_in_days parameter does not affect this command, as it only applies to time travel queries that reference historical data versions of tables or databases. The other options are not valid ways to recover the table. Option B is incorrect because creating a table as select * from xyz.public.ABC at {offset = > -60 60 24 8} will not work, as this query will try to access a historical data version of the ABC table that does not exist anymore after dropping the database. Option C is incorrect because creating a table clone xyz.public.ABC at {offset = > -3600 24*3} will not work, as this query will try to clone a historical data version of the ABC table that does not exist anymore after dropping the database. Option D is incorrect because creating a Snowflake Support case to restore the database and table from fail-safe will not work, as fail-safe is only available for disaster recovery scenarios and cannot be accessed by customers.

Question 2

When would a Data engineer use table with the flatten function instead of the lateral flatten combination?

  1. Option A:

    When TABLE with FLATTEN requires another source in the from clause to refer to

  2. Option B:

    When TABLE with FLATTEN requires no additional source m the from clause to refer to

  3. Option C:

    When the LATERAL FLATTEN combination requires no other source m the from clause to refer to

  4. Option D:

    When table with FLATTEN is acting like a sub-query executed for each returned row

Show answer and explanation

Correct answer: A

The TABLE function with the FLATTEN function is used to flatten semi-structured data, such as JSON or XML, into a relational format. The TABLE function returns a table expression that can be used in the FROM clause of a query. The TABLE function with the FLATTEN function requires another source in the FROM clause to refer to, such as a table, view, or subquery that contains the semi-structured data. For example:

SELECT t.value:city::string AS city, f.value AS population FROM cities t, TABLE(FLATTEN(input = > t.value:population)) f;

In this example, the TABLE function with the FLATTEN function refers to the cities table in the FROM clause, which contains JSON data in a variant column named value. The FLATTEN function flattens the population array within each JSON object and returns a table expression with two columns: key and value. The query then selects the city and population values from the table expression.

See all 5 free sample questions