The Databricks CLI allows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
Command Type: databricks compute create is the correct syntax for creating a new compute resource (cluster).
Runtime Version: '14.3.x-scala2.12' specifies the Databricks runtime to use.
Workers: --num-workers 5 sets the number of worker nodes to 5.
Node Type: --node-type-id i3.xlarge defines the hardware configuration.
Cluster Name: --cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
Option A (databricks clusters create ...)
Incorrect: databricks clusters create is not a valid command in the Databricks CLI v0.205.
The correct CLI command for cluster creation is databricks compute create.
Option B (databricks clusters add ...)
Incorrect: databricks clusters add is not a valid CLI command.
Option C (databricks compute add ...)
Incorrect: databricks compute add is not a valid CLI command.
Option D (databricks compute create ...)
Correct: databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime 14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer is D.
Reference:
Databricks CLI Documentation

The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

Comprehensive and Detailed
In Delta Lake, the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
By default, this retention duration is set to 7 days.
To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days') This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
B: This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
C: VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration.
D: Setting spark.conf applies a session-level configuration and does not permanently update the table's retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.