Databricks-Certified-Data-Engineer-Professional試験無料問題集「Databricks Certified Data Engineer Professional 認定」

A. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.

B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.

C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.

D. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.

E. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.

A. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

B. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

C. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

D. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.

E. Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.

A. 

B. return spark.readStream.table("bronze")

C. return spark.readStream.load("bronze")

D. def new_records():

E. return spark.read.option("readChangeFeed", "true").table ("bronze")

A. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

B. The current table schema does not contain the field valid coordinates; schema evolution will need Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from to be enabled before altering the table to add a constraint.

C. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

D. The activity details table already exists; CHECK constraints can only be added during initial table creation.

E. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

A. Can Read

B. Can Run

C. Can Manage

D. Can Edit

E. No permissions

A. date = dbutils.notebooks.getParam("date")

B. input_dict = input()
date= input_dict["date"]

C. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")

D. date = spark.conf.get("date")

E. import sys
date = sys.argv[1]

A. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.

B. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

C. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

D. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

E. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

A. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.

B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

C. Instruct the marketing team to download results as a CSV and email them to the sales organization.

D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

E. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

A. Stage's detail screen and Executor's log files

B. Driver's and Executor's log files

C. Stage's detail screen and Query's detail screen

D. Query's detail screen and Job's detail screen

E. Executor's detail screen and Executor's log files