When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

• A.The five Minute Load Average remains consistent/flat
• B.Bytes Received never exceeds 80 million bytes per second
• C.Network I/O never spikes
• D.Total Disk Space remains constant
• E.CPU Utilization is around 75%

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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.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.
• B.Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
• C.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.
• D.Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
• E.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.

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A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?

• A.All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
• B.All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
• C.The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
• D.Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
• E.All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

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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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A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

• A.They are merged.
• B.They are ignored.
• C.They are updated.
• D.They are inserted.
• E.They are deleted.

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