Databricks Data Engineer Professional Practice Exam in 2024

Databricks Certified Data Engineer Professional Certification is a highly sought-after credential in the field of data engineering. This certification is designed for individuals who have a strong background in data engineering and are looking to validate their skills and expertise in using Databricks to build and manage data pipelines, conduct data analysis, and optimize data processing workflows.

One of the key features of the Databricks Certified Data Engineer Professional Certification is the practice exam. This practice exam is designed to simulate the experience of taking the actual certification exam, allowing candidates to familiarize themselves with the format and types of questions that they may encounter. By taking the practice exam, candidates can assess their readiness for the certification exam and identify areas where they may need to focus their study efforts.

This certification exam itself covers a wide range of topics related to data engineering with Databricks, including data ingestion, data transformation, data storage, and data analysis. Candidates will be tested on their ability to design and implement data pipelines using Databricks, optimize data processing workflows for performance and scalability, and troubleshoot common issues that may arise during data processing.

To earn the Databricks Certified Data Engineer Professional Certification, candidates must pass the certification exam with a score of 70% or higher. This certification is valid for two years, after which candidates will need to recertify in order to maintain their certification status.

In addition to the practice exam, candidates preparing for the Databricks Certified Data Engineer Professional Certification can take advantage of a variety of resources to help them study and prepare for the exam. Databricks offers a range of training courses, study guides, and practice exercises to help candidates build their skills and knowledge in data engineering with Databricks.

Databricks Certified Data Engineer Professional Certification is a valuable credential for data engineers who work with Databricks. By earning this certification, candidates can demonstrate their expertise in using Databricks to build and manage data pipelines, conduct data analysis, and optimize data processing workflows. This certification can help data engineers advance their careers, increase their earning potential, and gain recognition for their skills and expertise in the field of data engineering.

Databricks Certified Data Engineer Professional Exam Summary:

• Exam Name : Databricks Certified Data Engineer Professional

• Type:Proctored certification

• Total number of questions :60

• Time limit :120 minutes

• Registration fee :$200

• Question types :Multiple choice

• Test aides :None allowed

• Languages :English, 日本語, Português BR

• Delivery method :Online proctored

• Prerequisites :None, but related training highly recommended

• Recommended experience :6+ months of hands-on experience performing the data engineering tasks outlined in the exam guide

• Validity period :2 years

Databricks Certified Data Engineer Professional Exam Syllabus Topics:

• Databricks Tooling – 20%

• Data Processing – 30%

• Data Modeling – 20%

• Security and Governance – 10%

• Monitoring and Logging – 10%

• Testing and Deployment – 10%

Databricks Tooling

• Explain how Delta Lake uses the transaction log and cloud object storage to guarantee atomicity and durability

• Describe how Delta Lake’s Optimistic Concurrency Control provides isolation, and which transactions might conflict

• Describe basic functionality of Delta clone.

• Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes

• Implement Delta tables optimized for Databricks SQL service

• Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)

Data Processing (Batch processing, Incremental processing, and Optimization)

• Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance

• Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)

• Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files.

• Articulate multiple strategies for updating 1+ records in a spark table (Type 1)

• Implement common design patterns unlocked by Structured Streaming and Delta Lake.

• Explore and tune state information using stream-static joins and Delta Lake

• Implement stream-static joins

• Implement necessary logic for deduplication using Spark Structured Streaming

• Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read

• Leverage CDF to easily propagate deletes

• Demonstrate how proper partitioning of data allows for simple archiving or deletion of data

• Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries

Data Modeling

• Describe the objective of data transformations during promotion from bronze to silver

• Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture

• Apply Delta Lake clone to learn how shallow and deep clone interact with source/target tables.

• Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads.

• Implement best practices when streaming data from multiplex bronze tables.

• Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver

• Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake

• Implement tables avoiding issues caused by lack of foreign key constraints

• Add constraints to Delta Lake tables to prevent bad data from being written

• Implement lookup tables and describe the trade-offs for normalized data models

• Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads.

• Implement SCD Type 0, 1, and 2 tables

Security & Governance

• Create Dynamic views to perform data masking

• Use dynamic views to control access to rows and columns

Monitoring & Logging

• Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications.

• Inspect event timelines and metrics for stages and jobs performed on a cluster

• Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications.

• Design systems that control for cost and latency SLAs for production streaming jobs.

• Deploy and monitor streaming and batch jobs

Testing & Deployment

• Adapt a notebook dependency pattern to use Python file dependencies

• Adapt Python code maintained as Wheels to direct imports using relative paths

• Repair and rerun failed jobs

• Create Jobs based on common use cases and patterns

• Create a multi-task job with multiple dependencies

• Design systems that control for cost and latency SLAs for production streaming jobs.

• Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters.

• Execute commands from the CLI to deploy and monitor Databricks jobs.

• Use REST API to clone a job, trigger a run, and export the run output

Overall, the Databricks Certified Data Engineer Professional Certification is a valuable credential for data engineers who work with Databricks. By earning this certification, candidates can demonstrate their expertise in using Databricks to build and manage data pipelines, conduct data analysis, and optimize data processing workflows. This certification can help data engineers advance their careers, increase their earning potential, and gain recognition for their skills and expertise in the field of data engineering.

DISCLAIMER :These questions are designed to, give you a feel of the level of questions asked in the actual exam. We are not affiliated with Databricks or Apache. All the screenshots added to the answer explanation are not owned by us. Those are added just for reference to the context.

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