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

Databricks Certified Data Engineer Professional Sample Questions:

1. An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A) Shallow clone the table for the analytics team.
B) Deep clone the table for the analytics team.
C) Give the analytics team direct access to the production table.
D) Create a new table for the analytics team using a CTAS statement.

2. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
B) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
C) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
D) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
E) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.

3. A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

A) Implement a write operation using MERGE INTO with a unique key
B) Use Delta Lake's change data feed to filter duplicate records
C) Configure Structured Streaming with dropDuplicates transformation
D) Use Delta Lake time travel to identify and remove duplicates

4. Which distribution does Databricks support for installing custom Python code packages?

A) sbt
B) CRAN
C) nom
D) Wheels
E) CRAM
F) jars

5. When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A) In the Spart UI, under the Jobs tab
B) In the Query Profiler, under the Stages tab
C) In the Spark UI, under the SQL/DataFrame tab
D) In the Query Profiler, under Query Source

Solutions: