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A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

• A. Post_time
• B. Post_id
• C. Date
• D. User_id

A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.
Which two actions should the data engineer take? (Choose 2)

• A. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
• B. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
• C. Periodically run OPTIMIZE table_name.
• D. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
• E. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• B. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• C. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
• D. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

• A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
• B. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
• C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
• D. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers , one driver of type i3.xlarge, and should use the ' 14.3.x-scala2.12 ' runtime.
Which command should the data engineer use?

• A. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• B. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• C. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• D. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name DataEngineer_cluster