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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Using Pandas API on Spark5%- Pandas API
  • 1. Interoperability with PySpark
  • 2. Pandas transformations
  • 3. Pandas on Spark DataFrames
Topic 2: Developing Apache Spark DataFrame API Applications30%- DataFrame Operations
  • 1. User Defined Functions
  • 2. Creating and transforming DataFrames
  • 3. Reading and writing data
  • 4. Working with complex data types
  • 5. Partitioning data
  • 6. Selecting and renaming columns
  • 7. Handling null values
Topic 3: Using Spark SQL20%- Spark SQL Operations
  • 1. Window functions
  • 2. Aggregations and grouping
  • 3. Built-in SQL functions
  • 4. Filtering and sorting data
  • 5. Joins and subqueries
Topic 4: Structured Streaming10%- Streaming Applications
  • 1. Structured Streaming concepts
  • 2. Streaming sources and sinks
  • 3. Triggers and checkpoints
  • 4. Output modes
Topic 5: Using Spark Connect to Deploy Applications5%- Spark Connect
  • 1. Client-server architecture
  • 2. Application deployment
  • 3. Remote Spark sessions
Topic 6: Troubleshooting and Tuning10%- Performance Optimization
  • 1. Execution plan analysis
  • 2. Shuffle optimization
  • 3. Caching and persistence
  • 4. Broadcast joins
Topic 7: Apache Spark Architecture and Components20%- Spark Architecture
  • 1. Driver and Executor roles
  • 2. Cluster managers
  • 3. Adaptive Query Execution
  • 4. Lazy evaluation

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. What is the benefit of using Pandas on Spark for data transformations?
Options:

A) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
B) It computes results immediately using eager execution, making it simple to use.
C) It is available only with Python, thereby reducing the learning curve.
D) It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.


2. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A) FATAL, NONE, INFO, DEBUG
B) ERROR, WARN, TRACE, OFF
C) WARN, NONE, ERROR, FATAL
D) ALL, DEBUG, FAIL, INFO


3. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Add more executor instances to the cluster
B) Increase the driver memory on the client machine
C) Switch the deployment mode to cluster mode
D) Switch the deployment mode to local mode


4. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write(path="/some/path").saveAsTable("default_table")
B) users.write.saveAsTable("default_table", path="/some/path")
C) users.write.saveAsTable("default_table").option("path", "/some/path")
D) users.write.option("path", "/some/path").saveAsTable("default_table")


5. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A) A stage contains multiple tasks, and each task contains multiple jobs.
B) A job contains multiple tasks, and each task contains multiple stages.
C) A stage contains multiple jobs, and each job contains multiple tasks.
D) A job contains multiple stages, and each stage contains multiple tasks.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: D

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