Free Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps Questions & Answers

A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing.
How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

• A.  Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
• B.  Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
• C.  Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
• D.  Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.

Which approach demonstrates a modular and testable way to use DataFrame.transform for ETL code in PySpark?

• A.  def transform_data(input_df):
  # transformation logic here
  return output_df
  test_input = spark.createDataFrame([(1, " a " )], [ " id " , " value " ]) assertDataFrameEqual(transform_data(test_input), expected)
• B.  class Pipeline:
  def transform(self, df):
  return df.withColumn( " value_upper " , upper(col( " value " )))
  pipeline = Pipeline()
  assertDataFrameEqual(pipeline.transform(test_input), expected)
• C.  def upper_value(df):
  return df.withColumn( " value_upper " , upper(col( " value " )))
  def filter_positive(df):
  return df.filter(df[ " id " ] > 0)
  pipeline_df = df.transform(upper_value).transform(filter_positive)
• D.  def upper_transform(df):
  return df.withColumn( " value_upper " , upper(col( " value " )))
  actual = test_input.transform(upper_transform)
  assertDataFrameEqual(actual, expected)

Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

• A.  Ensures that all steps interact correctly to achieve the desired end result
• B.  Validates a complete use case of your application
• C.  Troubleshooting is easier since all steps are isolated and tested individually
• D.  Yields faster deployment and execution times
• E.  Improves the quality of your data

Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution.
How should the data engineers resolve this merge conflict using Databricks Git folders?

• A.  Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
• B.  Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
• C.  Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
• D.  Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.

The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

• A.  Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
• B.  Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
• C.  Group members have full permissions on the prod database and can also assign permissions to other users or groups.
• D.  Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
• E.  Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.