Exam Certified Data Engineer Professional topic 1 question 227 discussion

Actual exam question from Databricks's Certified Data Engineer Professional

Question #: 227
Topic #: 1

[All Certified Data Engineer Professional Questions]

A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.

How can the data engineer fix this?

A. Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.
B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
C. Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.
D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Show Suggested Answer

Suggested Answer: C 🗳️

by benni_ale at Dec. 8, 2024, 11:02 p.m.

Comments

Submit Cancel

KadELbied

2 months ago

Selected Answer: B

suretly B

upvoted 1 times

...

Thameur01

6 months, 4 weeks ago

Selected Answer: C

here is a correct implementation: def create_table(t): @dlt.table(name=f"{t}_dataset") def table_definition(): return spark.read.table(t) tables = ["t1", "t2", "t3"] for t in tables: create_table(t)

upvoted 2 times

...

benni_ale

7 months ago

Selected Answer: C

Problem seems to be the fact that the new_table function has no parameter and the t variable won't be recognized as a variable.. However i have not tested it as DLT is not available in free membership. :(

upvoted 2 times

...