C2090-103 Apache Spark 1.6 Developer Exam Overview
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Section 1 - Architecture
12%
Compare and contrast Spark with Hadoop MapReduce
Explain memory management in Spark
Explain concepts such as master, drivers, executors, stages and tasks
Explain Spark transformations and actions with respect to lazy evaluation
Configure your application to run on a cluster
Section 2 - Performance and Troubleshooting
22%
Manage partitions to improve RDD performance and apply different partition strategies
Identify what operations cause shuffling
Optimize memory usage with serialization options
Use caching, checkpoint, and persistence in appropriate situations
Debug Spark code
Monitor Spark applications
Manage runtime issues and performance bottlenecks in Spark
Section 3 - Core Skills
48%
Read/writre data from multiple data sources and file types
Create and work with RDDs and related APIs
Create and work with DataFrames and related APIs
Create Spark config contexts for different requirements
Work with key value pairs and associated Spark APIs for key value pairs
Work with SparkSQL
Define and work with accumulators
Define and work with broadcast variables
Launch applications with spark-submit
Section 4 - Advanced Skills
18%
Build a pipeline with Streaming, MLLib, SQL, and Graph on Spark
Work with Spark Streaming APIs
Work with SparkML and MLLib APIs
Work with GraphX"