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

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.

The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.

Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

Options:

A.

The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

B.

Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.

C.

Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.

D.

Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

E.

Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

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Question 2

Which statement describes the correct use of pyspark.sql.functions.broadcast?

Options:

A.

It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

B.

It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

C.

It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

D.

It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

E.

It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

Question 3

The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.

The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.

The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.

Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

Options:

A.

Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.

B.

Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.

C.

Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

D.

Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.

E.

Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.