Online Access Free Databricks-Certified-Data-Engineer-Professional Practice Test

A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

• A.The total count of rows is calculated by scanning all data files
• B.The total count of rows will be returned from cached results unless REFRESH is run
• C.The total count of records is calculated from the Hive metastore
• D.The total count of records is calculated from the parquet file metadata
• E.The total count of records is calculated from the Delta transaction logs

The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

• A.Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
• B.Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
• C.Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
• D.Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
• E.Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

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

• A.It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
• B.It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
• C.It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
• D.It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
• E.It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

• A.Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
• B.Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
• C.Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
• D.Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
• E.Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A.Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• B.Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
• C.Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
• D.Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.