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

Section	Weight	Objectives
Delta Lake	20-25%	- Delta Lake operations 1. Merge, update, delete operations 2. Schema evolution and enforcement 3. Delta Live Tables - Delta Lake fundamentals 1. ACID transactions 2. Optimize and Z-order 3. Time travel and data versioning
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Differences between data lake, data warehouse, and lakehouse 3. Data governance fundamentals
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Jobs and job scheduling 3. Monitoring and alerting
Data Ingestion	15-20%	- Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals - Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?

A. CREATE DATABASE IF NOT EXISTS customer360 LOCATION '/customer/customer360';
B. CREATE DATABASE customer360 DELTA LOCATION '/customer/customer360';
C. CREATE DATABASE IF NOT EXISTS customer360;
D. CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION '/customer/customer360';
E. CREATE DATABASE customer360 LOCATION '/customer/customer360';

Answer: A

NEW QUESTION # 19
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?

A. Require the analytics team to use a tool that supports Delta table.
B. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
C. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
D. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.

Answer: C

Explanation:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
(A) Require the analytics team to use a tool that supports Delta table
Incorrect: While Delta Lake is widely used, requiring the team to change tools is not a flexible or scalable solution.
(B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table Incorrect:
The uniform feature must be enabled after conversion.
You cannot directly enable uniform without first converting the table.
(C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team Incorrect:
Creating a separate Iceberg copy would duplicate storage and increase maintenance complexity.
This is not necessary when Delta UniForm allows direct compatibility with Iceberg.
(D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table Correct:
The best approach is to convert the existing Delta table to Iceberg using the Databricks Delta to Iceberg migration tools.
After conversion, enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice for interoperability between Delta and Iceberg is to convert the Delta table to Iceberg and enable uniform, ensuring cross-compatibility without data duplication.
Thus, Option (D) is the correct answer.
Reference:
Delta UniForm for Apache Iceberg - Databricks Documentation
Convert Delta to Iceberg - Databricks

NEW QUESTION # 20
Review the following error traceback:
Which statement describes the error being raised?

A. The code executed was PvSoark but was executed in a Scala notebook.
B. There is a type error because a column object cannot be multiplied.
C. There is a type error because a DataFrame object cannot be multiplied.
D. There is no column in the table named heartrateheartrateheartrate
E. There is a syntax error because the heartrate column is not correctly identified as a column.

Answer: E

Explanation:
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns
'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as
'heartrate'. References: AnalysisException

NEW QUESTION # 21
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. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
B. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
C. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

Answer: D

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.
References:
* Databricks Documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 22
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of
512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
B. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
C. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
D. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Answer: D

NEW QUESTION # 23
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