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Databricks Certified Professional Data Engineer exam is a certification program designed for data professionals who want to validate their expertise in building and maintaining data pipelines using Databricks. Databricks is a cloud-based data engineering platform that provides a unified analytics engine for big data processing, machine learning, and streaming analytics. Databricks-Certified-Professional-Data-Engineer Exam is designed to test a candidate's ability to design, build, and optimize data pipelines using Databricks, as well as their proficiency in data modeling, data warehousing, and data integration.

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Databricks Certified Professional Data Engineer is a certification exam offered by Databricks for data engineers. Databricks-Certified-Professional-Data-Engineer exam evaluates the ability of a candidate to design and implement data solutions using Databricks. Databricks is a unified data analytics platform that enables data teams to collaborate on data engineering, machine learning, and analytics tasks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and proficiency of data engineers in using Databricks for data engineering tasks.

Databricks Certified Professional Data Engineer exam is a hands-on exam that requires the candidate to complete a set of tasks using Databricks. Databricks-Certified-Professional-Data-Engineer Exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to optimize and tune data pipelines for performance and reliability.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

Answer: C

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.
Reference:
Databricks Documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 90
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A. return spark.readStream.load( " bronze " )
B.
C. return spark.read.option( " readChangeFeed " , " true " ).table ( " bronze " )
D.
E. return spark.readStream.table( " bronze " )

Answer: B

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 91
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. Option A
B. Option D
C. Option B
D. Option E
E. Option C

Answer: C

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the "cloudFiles.
schemaLocation" option, which is required for the schema detection andevolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader: https://docs.databricks.com/en/ingestion
/auto-loader/schema.html
* Write streaming data: https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming- data.html

NEW QUESTION # 92
Which of the following SQL keywords can be used to append new rows to an existing Delta table?

A. COPY
B. UNION
C. INSERT INTO
D. DELETE
E. UPDATE

Answer: C

NEW QUESTION # 93
Given the following error traceback (from display(df.select(3*"heartrate"))) which shows AnalysisException: cannot resolve 'heartrateheartrateheartrate', which statement describes the error being raised?

A. There is a type error because a DataFrame object cannot be multiplied.
B. There is a syntax error because the heartrate column is not correctly identified as a column.
C. There is no column in the table named heartrateheartrateheartrate.
D. There is a type error because a column object cannot be multiplied.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "select() expects column names or Column expressions."
Exact extract: "Use col("name") (or df["name"]) to reference a column; Python string operations act on strings, not columns." In Python, "heartrate"*3 is string multiplication that yields "heartrateheartrateheartrate". select() then looks for a column literally named that string. Because no such column exists, Spark raises an analysis error that it "cannot resolve" that column. The correct expression to multiply the column is df.select((F.col("heartrate")*3).alias("heartrate_x3")).
Reference:

NEW QUESTION # 94
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