Databricks Certified Associate Developer for Apache Spark 3.0 : Associate-Developer-Apache-Spark

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最新的 Databricks Certification Associate-Developer-Apache-Spark 免費考試真題:

1. Which of the following code blocks writes DataFrame itemsDf to disk at storage location filePath, making sure to substitute any existing data at that location?

A) itemsDf.write.mode("overwrite").path(filePath)
B) itemsDf.write().parquet(filePath, mode="overwrite")
C) itemsDf.write(filePath, mode="overwrite")
D) itemsDf.write.option("parquet").mode("overwrite").path(filePath)
E) itemsDf.write.mode("overwrite").parquet(filePath)

2. Which of the following describes Spark actions?

A) Stage boundaries are commonly established by actions.
B) Actions are Spark's way of exchanging data between executors.
C) The driver receives data upon request by actions.
D) Writing data to disk is the primary purpose of actions.
E) Actions are Spark's way of modifying RDDs.

3. Which of the following statements about garbage collection in Spark is incorrect?

A) Optimizing garbage collection performance in Spark may limit caching ability.
B) Serialized caching is a strategy to increase the performance of garbage collection.
C) In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
D) Manually persisting RDDs in Spark prevents them from being garbage collected.
E) Garbage collection information can be accessed in the Spark UI's stage detail view.

4. Which of the following describes the characteristics of accumulators?

A) If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.
B) Accumulators are immutable.
C) Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.
D) Accumulators are used to pass around lookup tables across the cluster.
E) All accumulators used in a Spark application are listed in the Spark UI.

5. 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.persist(StorageLevel.MEMORY_ONLY)
B) from pyspark import StorageLevel
transactionsDf.cache(StorageLevel.MEMORY_ONLY)
C) transactionsDf.cache()
D) transactionsDf.clear_persist()
E) transactionsDf.persist()
F) transactionsDf.storage_level('MEMORY_ONLY')

問題與答案：

問題 #1 答案： E

問題 #2 答案： C

問題 #3 答案： D

問題 #4 答案： A

問題 #5 答案： A