A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

• A.Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
• B.The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
• C.Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
• D.One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
• E.The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

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The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

• A.Can Manage
• B.Can Edit
• C.No permissions
• D.Can Read
• E.Can Run

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Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

• A.spark.sql.files.maxPartitionBytes
• B.spark.sql.autoBroadcastJoinThreshold
• C.spark.sql.files.openCostInBytes
• D.spark.sql.adaptive.coalescePartitions.minPartitionNum
• E.spark.sql.adaptive.advisoryPartitionSizeInBytes

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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.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.
• B.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.
• C.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.
• D.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.
• E.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.

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The business intelligence team has a dashboard configured to track various summary metrics for retail stories.
This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

• A.Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
• B.Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
• C.Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
• D.Define a view against the products_per_order table and define the dashboard against this view.

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