Databricks Certified Professional Data Engineer Exam: Databricks-Certified-Professional-Data-Engineer Exam Premium File as PDF & Test Engine

A platform engineer must report resource consumption by SKU tier across every workspace.
The engineer chooses the system.billing.usage system table to build a query.
Which SQL query correctly returns daily usage by product?

• A. SELECT
  DATE_TRUNC('day', usage_start_time) AS usage_day,
  sku_name,
  SUM(usage_quantity) AS total_daily_dbus
  FROM system.billing.usage
  WHERE usage_quantity IS NOT NULL
  GROUP BY usage_day, sku_name
  ORDER BY usage_day;
• B. SELECT
  CAST(usage_start_time AS DATE) AS usage_day,
  sku_name,
  COUNT(usage_quantity) AS dbu_count
  FROM system, billing.usage
  GROUP BY usage_day, sku_name
  ORDER BY usage_day;
• C. SELECT
  DATE_TRUNC('day', usage_start_time) AS day,
  sku_name,
  SUM(dbus) AS daily_dbus
  FROM system.billing.usage
  WHERE sku_name IS NOT NULL
  GROUP BY day, sku_name;
• D. SELECT
  TO_DATE(usage_start_time) AS usage_day,
  sku_name,
  SUM(usage_quantity) AS total_dbus
  FROM system.billing.usage
  GROUP BY usage_day
  ORDER BY usage_day;

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. 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.
• B. 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.
• 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. 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.
• E. 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.

Review the following error traceback:

Which statement describes the error being raised?

• A. There is a type error because a column object cannot be multiplied.
• B. There is no column in the table named heartrateheartrateheartrate
• C. There is a type error because a DataFrame object cannot be multiplied.
• D. There is a syntax error because the heartrate column is not correctly identified as a column.
• E. The code executed was PvSoark but was executed in a Scala notebook.

A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?

• A. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
• B. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requiresSetting multiple fields in a single update.
• C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
• D. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.