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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Related Certifications:	Databricks Certified Data Engineer Associate
Certificate Validity Period:	2 years
Exam Format:	Multiple select, Scenario-based questions, Multiple choice
Exam Duration:	120 minutes
Exam Price:	USD 200
Passing Score:	70%
Available Languages:	English
Real Exam Qty:	60-70
Recommended Training:	Databricks Academy
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam
Pre Condition:	Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer Exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which of the following command can be used to drop a managed delta table and the underlying files in the storage?

A. DROP TABLE table and run VACUUM command
B. DROP TABLE table_name
C. Use DROP TABLE table_name command and manually delete files using com-mand dbutils.fs.rm("/path",True)
D. DROP TABLE table_name CASCADE
E. DROP TABLE table_name INCLUDE_FILES

Answer: B

Explanation:
Explanation
The answer is DROP TABLE table_name,
When a managed table is dropped, the table definition is dropped from metastore and everything including data, metadata, and history are also dropped from storage.

NEW QUESTION # 51
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. userLookup.join(streamingDF, ["user_id"], how="right")
B. userLookup.join(streamingDF, ["userid"], how="inner")
C. streamingDF.join(userLookup, ["user_id"], how="outer")
D. streamingDF.join(userLookup, ["user_id"], how="left")
E. streamingDF.join(userLookup, ["userid"], how="inner")

Answer: A

Explanation:
In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins.
Reference:
Structured Streaming Programming Guide: Join Operations
Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins

NEW QUESTION # 52
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
B. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
E. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.

Answer: D

Explanation:
Explanation
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 53
A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing.
Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A. import dlt
@dlt.table
def raw_customers():
return spark.read.format( " csv " ).load( " s3://my-bucket/raw-customers/ " )
B. import dlt
@dlt.view
def raw_customers():
return spark.format.json( " s3://my-bucket/raw-customers/ " )
C. import dlt
@dlt.table
def raw_customers():
return spark.read.json( " s3://my-bucket/raw-customers/ " )
D. import dlt
@dlt.table
def raw_customers():
return spark.read.format( " parquet " ).load( " s3://my-bucket/raw-customers/ " )

Answer: C

Explanation:
The correct method to define a table using Lakeflow Declarative Pipelines (LDP) is with the @dlt.table decorator, which persists the output as a managed Delta table. When ingesting raw JSON data, spark.read.
json() or spark.read.format( " json " ).load() is the standard approach. This reads JSON-formatted files from the source and stores them in Delta format automatically managed by Databricks.
Reference Source: Databricks Lakeflow Declarative Pipelines Developer Guide - "Create tables from raw JSON and Delta sources."

NEW QUESTION # 54
A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
C. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.
D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
E. Records that violate the expectation cause the job to fail.

Answer: A

Explanation:
Explanation
The answer is, Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence

NEW QUESTION # 55
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