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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A security analytics pipeline must enrich billions of raw connection logs with geolocation data. The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events (≈ 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges (≈ 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

• A.Increase spark.sql.shuffle.partitions from 200 to 10000.
• B.Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
• C.Force a sort-merge join with /*+ MERGE(r) */.
• D.Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.

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The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

• A.Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
• B.Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
• C.Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
• D.Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

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The team has decided to take advantage of table properties to identify a business owner for each table, which of the following table DDL syntax allows you to populate a table property identifying the business owner of a table CREATE TABLE inventory (id INT, units FLOAT)

• A.SET TBLPROPERTIES business_owner = 'supply chain'
  CREATE TABLE inventory (id INT, units FLOAT)
• B.TBLPROPERTIES (business_owner = 'supply chain')
• C.CREATE TABLE inventory (id INT, units FLOAT)
  SET (business_owner = 'supply chain')
• D.CREATE TABLE inventory (id INT, units FLOAT)
  SET PROPERTY (business_owner = 'supply chain')
• E.CREATE TABLE inventory (id INT, units FLOAT)
  SET TAG (business_owner = 'supply chain')

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The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

• A.preds.write.mode("append").saveAsTable("churn_preds")
• B.
• C.
• D.preds.write.format("delta").save("/preds/churn_preds")
• E.

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