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The Databricks Certified Associate Developer for Apache Spark 3.0 certification exam assesses an understanding of the basics of the Spark architecture and the ability to apply the Spark DataFrame API to complete individual data manipulation tasks.

The Databricks Certified Associate Developer for Apache Spark 3.0 certification exam assesses the understanding of the Spark DataFrame API and the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. These tasks include selecting, renaming and manipulating columns; filtering, dropping, sorting, and aggregating rows; handling missing data; combining, reading, writing and partitioning DataFrames with schemas; and working with UDFs and Spark SQL functions. In addition, the exam will assess the basics of the Spark architecture like execution/deployment modes, the execution hierarchy, fault tolerance, garbage collection, and broadcasting.

1. have a basic understanding of the Spark architecture, including Adaptive Query Execution

2. be able to apply the Spark DataFrame API to complete individual data manipulation task, including: 

● selecting, renaming and manipulating columns

● filtering, dropping, sorting, and aggregating rows

● joining, reading, writing and partitioning DataFrames

● working with UDFs and Spark SQL functions

Number of questions: 60 multiple-choice questions

Duaration: 120 minutes

Passing score:  70%

The exam will be conducted via an online proctor

Programming language: Python or Scala

Cost: 200.00 USD

Spark Architecture: Conceptual understanding (~17%)

Spark Architecture: Applied understanding (~11%)

Spark DataFrame API Applications (~72%)

1.Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?

A. itemsDf.write.avro(fileLocation)

B. itemsDf.write.format("avro").mode("ignore").save(fileLocation)

C. itemsDf.write.format("avro").mode("errorifexists").save(fileLocation)

D. itemsDf.save.format("avro").mode("ignore").write(fileLocation)

E. spark.DataFrameWriter(itemsDf).format("avro").write(fileLocation)

Answer: A

Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?

A. transactionsDf.sort(asc(value)).show(10)

B. transactionsDf.sort(col("value")).show(10)

C. transactionsDf.sort(col("value").desc()).head()

D. transactionsDf.sort(col("value").asc()).print(10)

E. transactionsDf.orderBy("value").asc().show(10)

Answer: B

Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

A. from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)

B. transactionsDf.cache()

C. transactionsDf.storage_level('MEMORY_ONLY')

D. transactionsDf.persist()

E. transactionsDf.clear_persist()

F. from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)

Answer: F

Which of the following is the deepest level in Spark's execution hierarchy?

A. Job

B. Task

C. Executor

D. Slot

E. Stage

Answer: B

Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

A. Slot is another name for executor.

B. There must be less executors than tasks.

C. An executor runs on a single core.

D. There must be more slots than tasks.

E. Tasks run in parallel via slots.

Which of the following statements about broadcast variables is correct?

A. Broadcast variables are serialized with every single task.

B. Broadcast variables are commonly used for tables that do not fit into memory.

C. Broadcast variables are immutable.

D. Broadcast variables are occasionally dynamically updated on a per-task basis.

E. Broadcast variables are local to the worker node and not shared across the cluster.

Answer: C