Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. What statement is true regarding the retention of job run history?

A) It is retained until you export or delete job run logs
B) It is retained for 60 days, after which logs are archived
C) It is retained for 90 days or until the run-id is re-used through custom run configuration
D) It is retained for 60 days, during which you can export notebook run results to HTML
E) It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

2. A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A) Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B) Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
C) Use only folder-level permissions and avoid setting permissions on individual jobs.
D) Grant all users CAN MANAGE permission on all jobs to avoid access issues.

3. A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A)

B)

C) return spark.readStream.load("bronze")
D) return spark.readStream.table("bronze")
E) return spark.read.option("readChangeFeed", "true").table ("bronze")

4. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A)

B)

C)

D)

5. A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A) spark.sql("VACUUM my_table RETAIN 15 HOURS")
B) spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
C) from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
D) spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")

Solutions: