A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

• A.The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
• B.Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
• C.Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
• D.Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
• E.The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

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A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

• A.Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
• B.Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY
• C.Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
• D.Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)

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The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

• A.Group members have full permissions on the prod database and can also assign permissions to other users or groups.
• B.Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
• C.Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
• D.Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
• E.Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.

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A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

• A.All records are cached to an operational database and then the filter is applied
• B.The Parquet file footers are scanned for min and max statistics for the latitude column
• C.All records are cached to attached storage and then the filter is applied
• D.The Delta log is scanned for min and max statistics for the latitude column
• E.The Hive metastore is scanned for min and max statistics for the latitude column

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The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

• A.DESCRIBE EXTENDED dev.pii test
• B.DESCRIBE DETAIL dev.pii test
• C.SHOW TBLPROPERTIES dev.pii test
• D.DESCRIBE HISTORY dev.pii test
• E.SHOW TABLES dev

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