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Databricks Certified Associate Developer for Apache Spark — Requirements & Free Practice

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Databricks Certified Associate Developer for Apache Spark

Licensing authority: Databricks

Initial by examination
Databricks Certified Associate Developer for Apache Spark
Scope
Initial Databricks Certified Associate Developer for Apache Spark certification through the current examination.

Databricks Certified Associate Developer for Apache Spark

Exam questions
Total questions: 45–55; Scored questions: 45; Unscored questions: 0–10; The total includes the scored and unscored questions.
Examination outline
  • Apache Spark Architecture and Components (20%)
    • Advantages and challenges of implementing Spark
    • Core Spark architecture components
    • Spark architecture, DataFrames, Datasets, sessions, caching, and memory
    • Spark execution hierarchy
    • Distributed partitioning and shuffles
    • Actions, transformations, and lazy evaluation
    • Apache Spark modules
  • Using Spark SQL (20%)
    • Read and write DataFrames with common sources
    • SQL queries on files and save modes
    • Persistent tables, sorting, and partitioning
    • Temporary views and SQL queries
  • Developing Apache Spark DataFrame/DataSet API Applications (30%)
    • Column, row, and table manipulation
    • DataFrame deduplication and validation
    • DataFrame aggregate operations
    • Date data type operations
    • DataFrame joins and unions
    • DataFrame input, output, and schemas
    • Sorting, schema inspection, and format conversion
    • User-defined functions and stateful operators
    • Broadcast variables and accumulators
    • Broadcast joins
  • Troubleshooting and Tuning Apache Spark DataFrame API Applications (10%)
    • Performance tuning and cluster utilization
    • Adaptive Query Execution
    • Spark application logging and monitoring
  • Structured Streaming (10%)
    • Structured Streaming engine and semantics
    • Streaming DataFrames, Datasets, outputs, and sinks
    • Streaming selection, projection, windows, and aggregation
    • Streaming deduplication and watermarks
  • Using Spark Connect to Deploy Applications (5%)
    • Spark Connect features
    • Client, Cluster, and Local deployment modes
  • Using Pandas API on Spark (5%)
    • Advantages of Pandas API on Spark
    • Pandas UDF creation and invocation
Examination sources