Databricks Certified Associate Data Engineer Exam Notes

Databricks Certified Associate Data Engineer Exam Overview

  • Target Audience:
    • Data Engineers
    • Beginner-level certification
    • Equivalent to six months of experience with data engineering using Databricks.

Exam Expectations for Associate Data Engineer

  • Skills Required:
    • Understanding of the Databricks Lakehouse Platform and its tools
    • Ability to build ETL pipelines using Apache Spark SQL and Python
    • Incremental data processing
    • Building production pipelines for data engineering applications
    • Familiarity with Databricks SQL queries and dashboards
    • Knowledge of and adherence to best security practices.

Exam Platform Information

  • The certification is offered through Kryterion's Webassessor platform.
  • Participants will be monitored via webcam and must provide valid photo ID.
  • No test aids are allowed during the exam.

Exam Grading

  • Exams are automatically graded.
  • Results, along with proctor session notes, will be reviewed, and it will typically take one week to receive results.

Exam Format and Structure

  • The exam consists of 45 multiple-choice questions categorized as follows:
    • Databricks Lakehouse Platform: 24% (11 Questions)
    • ELT with Spark SQL and Python: 29% (13 Questions)
    • Incremental Data Processing: 22% (10 Questions)
    • Production Pipelines: 16% (7 Questions)
    • Data Governance: 9% (4 Questions)

Databricks Lakehouse Platform Details (24%)

  • Understanding the benefits of the Databricks Lakehouse Platform which includes:
    • Clusters
    • Databricks File System (DBFS)
    • Notebooks and Repositories
    • Delta Lake concepts including management, manipulation, and optimization.
Key Features of a Lakehouse
  • Supports diverse data types and formats
  • Allows BI tools to directly interact with source data
  • Supports various workloads (BI, Data Science, ML, and Analytics)
  • Ensures data reliability and consistency.

Exam Practice Questions

  • Lakehouse Benefits: Identify benefits of a data lakehouse over a traditional data warehouse.
  • Understand how Delta Lake enables features like time travel/data snapshots and data reliability for analytics.

Cluster Management in Databricks (28%)

  • Cluster Types:
    • All-Purpose Clusters
    • Job Clusters
  • Clusters are composed of:
    • Driver (coordinates executors)
    • Executors (perform tasks).

Incremental Data Processing (22%)

  • Key Concepts:
    • Structured Streaming: triggers and general concepts
    • Auto Loader: for streaming reads
    • Multi-hop Architecture (Bronze-Silver-Gold tables).
Example Use of Auto Loader
  • Syntax to define streaming reads with Auto Loader: 
  df = spark.readStream 
        .format("cloudFiles") 
        .option("cloudFiles.format", "json") 
        .load("/input/path")
  • Stream writes handled similarly with checkpointing to manage state.

Delta Lake Concepts

  • Essential features of Delta Lake:
    • ACID Transactions
    • Unified batch and streaming
    • Data reliability through a transactional log.
  • Techniques like compaction, data skipping, and Z-ordering enhance performance.

Production Pipelines (16%)

  • Candidates should be able to build production pipelines including:
    • Databricks Jobs
    • Automation
    • Query scheduling and alerting
    • User interface and dashboards.
    • Understanding the use of cluster pools to expedite job initiation.

Data Governance (9%)

  • Utilizing the Unity Catalog for data governance practices which include:
    • Configuring access and permissions
    • Managing entity permissions, such as on tables and views.

Recommendations for Exam Preparation

  • Utilize training materials, participate in certification preparation workshops, and refer to Databricks documentation for detailed features and practices.