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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Modeling and Storage	- Schema evolution and data partitioning strategies - Delta Lake table design and optimization - Design scalable data lakehouse architectures
Topic 2: Production Pipelines and Orchestration	- Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs
Topic 3: Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control
Topic 4: Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. import sys
date = sys.argv[1]
B. date = spark.conf.get("date")
C. input_dict = input()
date= input_dict["date"]
D. date = dbutils.notebooks.getParam("date")
E. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")

Answer: D

Explanation:
Explanation
This is the correct way to get a parameter passed to a notebook by the Databricks Jobs API. The dbutils.notebooks.getParam method returns the value of a parameter passed to a notebook as a string. If no parameter with that name is passed, it returns None by default. You can also specify a default value as a second argument. Verified References: Databricks Certified Data Engineer Professional, under "Databricks Tooling" section; Databricks Documentation, under "Pass parameters to a notebook" section.

NEW QUESTION # 33
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

A. Reduce the size of the SQL Cluster size
B. Reduce the max size of auto scaling from 10 to 5
C. Setup the dashboard refresh schedule to end in two weeks
D. Change the spot instance policy from reliability optimized to cost optimized
E. Always use X-small cluster

Answer: C

Explanation:
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks

NEW QUESTION # 34
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job's schedule and configuration?

A. They can submit the Job once on a Job cluster.
B. They can submit the Job once on an all-purpose cluster.
C. They can download the JSON equivalent of the job from the Job's page.
D. They can download the XML description of the Job from the Job's page
E. They can link the Job to notebooks that are a part of a Databricks Repo.

Answer: B

NEW QUESTION # 35
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 namedpredswith 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?

A. preds.write.format("delta").save("/preds/churn_preds")
B. preds.write.mode("append").saveAsTable("churn_preds")
C.
D.
E.

Answer: B

NEW QUESTION # 36
A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = ' cancelled ' ;
What should be the behavior of deletion vectors when the command is executed?

A. Files are physically rewritten without the deleted rows.
B. Delta automatically removes all cancelled orders permanently.
C. Rows are marked as deleted in metadata, not in files.
D. Rows are marked as deleted both in metadata and in files.

Answer: C

Explanation:
Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: "With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites." Thus, rows are marked as deleted in metadata-not in files.

NEW QUESTION # 37
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