Databricks Certified Data Engineer Associate Exam (2023)
Databricks Certified Data Engineer Associate Practice Exam With Detailed Explanations!
What you will learn
Utilize Spark SQL and Python for ELT operations on the Databricks Lakehouse Platform.
Implement incremental data processing strategies to efficiently process large and continuously changing datasets.
Develop production pipelines to automate data processing and facilitate continuous delivery of data.
Apply data governance best practices to ensure data quality, security, and compliance on the Databricks Lakehouse Platform.
Why take this course?
These Databricks Certified Data Engineer Associate Practice Exams are specifically designed to assist you in comprehending all of the exam topics so that you can succeed in the actual exam, as well as learn the concepts of the Databricks platform. Our aim is not solely to assist you in passing the exam, but also to help you learn the Databricks platform along the way.
The Databricks Certified Data Engineer Associate certification exam evaluates an individual's ability to complete basic data engineering tasks using the Databricks Lakehouse Platform. It assesses one's understanding of the platform's architecture, capabilities, and workspace, as well as the ability to carry out multi-hop architecture ETL tasks utilizing Apache Spark SQL and Python in both batch and incremental processing paradigms. Additionally, it examines the tester's ability to put basic ETL pipelines and Databricks SQL queries and dashboards into production while maintaining entity permissions. Those who pass this certification exam can be expected to complete basic data engineering tasks using Databricks and its associated tools.
Minimally Qualified Candidate
The minimally qualified candidate should be able to:
Understand how to use and the benefits of using the Databricks Lakehouse Platform and its tools, including:
Data Lakehouse (architecture, descriptions, benefits)
Data Science and Engineering workspace (clusters, notebooks, data storage)
Delta Lake (general concepts, table management and manipulation, optimizations)
Build ETL pipelines using Apache Spark SQL and Python, including:
Relational entities (databases, tables, views)
ELT (creating tables, writing data to tables, cleaning data, combining and reshaping tables, SQL UDFs)
Python (facilitating Spark SQL with string manipulation and control flow, passing data between PySpark and Spark SQL)
Incrementally process data, including:
Structured Streaming (general concepts, triggers, watermarks)
Auto Loader (streaming reads)
Multi-hop Architecture (bronze-silver-gold, streaming applications)
Delta Live Tables (benefits and features)
Build production pipelines for data engineering applications and Databricks SQL queries and dashboards, including:
Jobs (scheduling, task orchestration, UI)
Dashboards (endpoints, scheduling, alerting, refreshing)
Understand and follow best security practices, including:
Unity Catalog (benefits and features)
Entity Permissions (team-based permissions, user-based permissions)
The certification exam is 90 minutes long and consists of 45 multiple-choice questions distributed by high-level topic. The questions will cover Databricks Lakehouse Platform, ELT with Spark SQL and Python, incremental data processing, production pipelines, and data governance. By completing these practice exams, you will be better prepared to tackle the actual certification exam and excel in your data engineering career.