Try Databricks : Databricks-Certified-Data-Engineer-Professional valid & accurate questions and answers

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. 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.
• B. 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.
• C. 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.
• D. 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.
• E. 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.

The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

• A. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
• B. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
• C. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
• D. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
• E. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

• A. fs
• B. libraries
• C. jobs
• D. workspace
• E. configure

A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

• A. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
• B. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
• C. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
• D. All records from all columns will be displayed with the values in user_ltv.
• E. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.

A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

• A. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
• B. Delete the failed run, disable the cluster policy, and re-execute all tasks.
• C. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
• D. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.