A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer
"Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

• A.Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
• B.The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
• C.Other than the default "admins" group, only individual users can be granted privileges on jobs.
• D.A user can only transfer job ownership to a group if they are also a member of that group.
• E.Only workspace administrators can grant "Owner" privileges to a group.

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The default threshold of VACUUM is 7 days, internal audit team asked to certain tables to maintain at least
365 days as part of compliance requirement, which of the below setting is needed to implement.

• A.ALTER TABLE table_name set TBLPROPERTIES (del-ta.deletedFileRetentionDuration= 'interval 365 days')
• B.MODIFY TABLE table_name set TBLPROPERTY (delta.maxRetentionDays = 'inter-val 365 days')
• C.ALTER TABLE table_name set EXENDED TBLPROPERTIES (del-ta.deletedFileRetentionDuration=
  'interval 365 days')
• D.ALTER TABLE table_name set EXENDED TBLPROPERTIES (delta.vaccum.duration= 'interval 365 days')

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The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

• A.Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
• B.The activity details table already exists; CHECK constraints can only be added during initial table creation.
• C.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.
• D.The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
• E.The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

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You are trying to calculate total sales made by all the employees by parsing a complex struct data type that stores employee and sales data, how would you approach this in SQL Table definition, batchId INT, performance ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>, in-sertDate TIMESTAMP Sample data of performance column
1.[
2.{ "employeeId":1234
3."sales" : 10000},
4.
5.{ "employeeId":3232
6."sales" : 30000}
7.]
Calculate total sales made by all the employees?
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate

• A.1.WITH CTE as (SELECT EXPLODE (performance) FROM table_name)
  2.SELECT SUM (performance.sales) FROM CTE
• B.1.WITH CTE as (SELECT FLATTEN (performance) FROM table_name)
  2.SELECT SUM (sales) FROM CTE
• C.1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
  2.as total_sales from sales
• D.SELECT SUM(SLICE (performance, sales)) FROM employee
• E.1.select reduce(flatten(collect_list(performance:sales)), 0, (x, y) -> x + y)
  2.as total_sales from sales

Correct Answer: C

Explanation
The answer is
1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
Nested Struct can be queried using the . notation performance.sales will give you access to all the sales values in the performance column.
Note: option D is wrong because it uses performance:sales not performance.sales. ":" this is only used when referring to JSON data but here we are dealing with a struct data type. for the exam please make sure to understand if you are dealing with JSON data or Struct data.

Other solutions:
we can also use reduce instead of aggregate
select reduce(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y) as total_sales from sales we can also use explode and sum instead of using any higher-order funtions.
1.with cte as (
2. select
3. explode(flatten(collect_list(performance.sales))) sales from sales
4.)
5.select
6. sum(sales) from cte
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate

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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.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.
• B.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.
• C.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.
• 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.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.

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