Question 1

A data engineer is analyzing a Spark job via the Spark UI. They have the following summary metrics for 27 completed tasks in a particular stage

Which conclusion can the data engineer draw from the above statistics ?

Options :

A : All task are operating over partitions with even amounts of data

B : All task are operating over empty or near empty partitions

C : All tasks are operating over partitions with larger skewed amounts of data.

D : Number of tasks are operating over partitions with larger skewed amounts of data.

E : Number of tasks are operating over empty or near empty partitions

Answer: D

Question 2

A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context. Assuming the job is already deployed, they need to trigger its execution while ensuring the target-specific configuration is respected. Which command will trigger the job execution?

Options :

A : databricks execute my_project_job -e prod

B : databricks job run my_project_job --env prod

C : databricks run my_project_job -t prod

D : databricks bundle run my_project_job -t prod

Answer: D

Question 3

A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A

metadata JSON file defines data quality rules for multiple tables, including:

{

"claims": [

{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},

{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}

]

}

The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.

How should the data engineer achieve this?

Options :

A : Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.

B : Invoke an external API to validate records against the metadata rules.

C : Reference each expectation with @dlt.expect decorators in the table declaration.

D : Use a SQL CONSTRAINT block referencing the JSON file path.

Answer: A

Question 4

The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data. Which statement is a possible explanation for this behavior?

Options :

A : %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time

B :

Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

C : %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

D :

Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala

E :

%sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

Answer: C

Question 5

Which of the following describes Cron syntax in Databricks Jobs ?

Options :

A : It’s an expression to represent the maximum concurrent runs of a job

B : It’s an expression to represent the retry policy of a job

C : It’s an expression to describe the email notification events (start, success, failure)

D : It’s an expression to represent the run timeout of a job

E : It’s an expression to represent complex job schedule that can be defined programmatically

Answer: E

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