Databricks Certified Professional Data Engineer - Databricks-Certified-Professional-Data-Engineer Exam Practice Test

The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill . The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)

In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization.
Which two configurations and practices are recommended to incrementally ingest these images into the table?
(Choose 2 answers)

A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that over time, data being transmitted from the bicycle sensors fails to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
" valid_lat " : " (lat IS NOT NULL) " ,
" valid_long " : " (long IS NOT NULL) "
}
quarantine_rules = " NOT({0}) " .format( " AND " .join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table( " ride_and_go.telemetry.trips " )
How should the data engineer meet the requirements to capture good and bad data?

A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

A data engineer is using Lakeflow Spark Declarative Pipelines Expectations to track the data quality of incoming sensor data. Periodically, sensors send bad readings that are out of range, and the team is currently flagging those rows with a warning and writing them to the silver table along with the good data. They have been given a new requirement: the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for the silver table:
@dlt.table
@dlt.expect( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
Which code will satisfy the requirements?

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 named preds with 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?