Databricks-Certified-Professional-Data-Engineer Exam: Absolutely Free Online Practice

Question 1
- Which of the following statements regarding the retention policy of Delta lake CDF is correct ?
  A : Running the VACUUM command on the table deletes CDF data as well
  
  B : Running the VACUUM command on the table does not deletes CDF data
  
  C : Running the VACUUM command on the table does not deletes CDF data unless CASCADE clause is set to true
  
  D : CDF data files can be purged by running VACUUM CHANGES command
  
  E : CDF data files can never be permanently purged from Delta Lake
  
  Answer: A
Question 2
- A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively. Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?
  A : ala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs, all PySpark and Spark SQL logic should be refactored.
  
  B : only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
  
  C : oduction code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
  
  D : lling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
  
  E : Jobs UI should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
  
  Answer: D
Question 3
- Which of the following describes Cron syntax in Databricks Jobs ?
  A : It’s an expression to represent the maximum concurrent runs of a job
  
  B : It’s an expression to represent the retry policy of a job
  
  C : It’s an expression to describe the email notification events (start, success, failure)
  
  D : It’s an expression to represent the run timeout of a job
  
  E : It’s an expression to represent complex job schedule that can be defined programmatically
  
  Answer: E
Question 4
- Which of the following commands allows data engineers to perform an insert-only merge?
  A : MERGE INTO orders
  USING new_orders
  ON orders.orders_id = new_orders.orders_id
  WHEN MATCHED
  INSERT *
  
  B : MERGE INTO orders
  USING new_orders
  ON orders.orders_id = new_orders.orders_id
  WHEN NOT MATCHED
  INSERT *
  
  C : MERGE INTO orders
  USING new_orders
  ON orders.orders_id = new_orders.orders_id
  WHEN MATCHED
  INSERT *
  WHEN NOT MATCHED
  IGNORE *
  
  D : MERGE INTO orders
  USING new_orders
  ON orders.orders_id = new_orders.orders_id
  WHEN NOT MATCHED
  INSERT *
  WHEN MATCHED
  IGNORE *
  
  E : MERGE INTO orders
  USING new_orders
  ON orders.orders_id = new_orders.orders_id
  WHEN NOT MATCHED
  INSERT *
  ELSE IGNORE *
  
  Answer: B
Question 5
- Which statement describes Delta Lake optimized writes?
  
  A : Before a Jobs cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
  
  B : An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an OPTIMIZE job is executed toward a default of 1 GB.
  
  C : Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
  
  D : Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
  
  E : A shuffle occurs prior to writing to try to group similar data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
  
  Answer: E

Free Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions

Absolute Free Databricks-Certified-Professional-Data-Engineer Exam Practice for Comprehensive Preparation