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Databricks Certified Associate Developer for Apache Spark Syllabus

Apache Spark Architecture and Components

How Spark executes work across drivers, executors and partitions.

20%
Weight
9
Questions
20
Marks

Cluster Components

  • Driver, executors, cores and memory
  • SparkSession lifecycle
  • Execution hierarchy: jobs, stages and tasks
  • Spark modules (Core, SQL, Streaming, MLlib, Pandas API)

Execution Concepts

  • Transformations vs. actions
  • Lazy evaluation
  • Partitioning and shuffles
  • Caching, storage levels and garbage collection

Using Spark SQL

Reading, writing and querying data with Spark SQL.

20%
Weight
9
Questions
20
Marks

Reading and Writing

  • Reading from JDBC and file sources
  • Save modes and overwriting
  • partitionBy when writing
  • Persistent tables with sorting and partitioning

Querying

  • Running SQL directly on ORC, JSON, CSV, text and Delta files
  • Temporary views from DataFrames
  • Querying views with SQL
  • Global vs. local temporary views

Developing Apache Spark DataFrame/Dataset API Applications

Manipulating, combining and aggregating DataFrames in Python.

30%
Weight
14
Questions
30
Marks

Column and Row Operations

  • Adding, dropping, renaming and splitting columns
  • Filtering rows and exploding arrays
  • Deduplication and validation
  • Date handling and Unix epoch conversion

Combining and Aggregating

  • Inner, left, broadcast, multi-key and cross joins
  • union and unionAll
  • Aggregations including approx_count_distinct
  • Reading and writing with explicit schemas

Advanced API Features

  • Creating and invoking UDFs
  • Broadcast variables
  • Accumulators
  • Sorting, iterating and converting DataFrames to lists

Troubleshooting and Tuning Apache Spark DataFrame API Applications

Improving performance and diagnosing failures.

10%
Weight
5
Questions
10
Marks

Performance Tuning

  • repartition vs. coalesce
  • Detecting data skew
  • Reducing shuffles
  • Adaptive Query Execution (AQE)

Diagnostics

  • Driver and executor logs
  • Out-of-memory errors
  • Cluster underutilization
  • Spark UI stages and tasks for diagnosis

Structured Streaming

Building streaming DataFrames and stateful stream operations.

10%
Weight
4
Questions
10
Marks

Streaming Model

  • Structured Streaming engine and programming model
  • Micro-batch processing
  • Exactly-once semantics
  • Fault tolerance and checkpoints

Streaming Operations

  • Creating and writing streaming DataFrames
  • Output modes and sinks
  • Window and aggregation operations
  • Streaming deduplication with and without watermarks

Using Spark Connect to Deploy Applications

Spark Connect features and deployment modes.

5%
Weight
2
Questions
5
Marks

Spark Connect Architecture

  • Client-server architecture of Spark Connect
  • Spark Connect features
  • Remote SparkSession creation
  • Benefits over classic driver applications

Deployment Modes

  • Client deployment mode
  • Cluster deployment mode
  • Local mode
  • Choosing a deployment mode

Using Pandas API on Apache Spark

Using pandas-style APIs and Pandas UDFs at scale.

5%
Weight
2
Questions
5
Marks

Pandas API on Spark

  • Advantages of the Pandas API on Spark
  • pyspark.pandas DataFrames
  • Converting between pandas-on-Spark and Spark DataFrames
  • Scaling pandas workloads

Pandas UDFs

  • Creating Pandas UDFs
  • Invoking Pandas UDFs
  • Series-to-Series Pandas UDFs
  • Arrow-based data transfer

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Learning Progress

Apache Spark Architecture and Components 85%
Using Spark SQL 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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