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

1. Which of the following describes a valid concern about partitioning?

A) A shuffle operation returns 200 partitions if not explicitly set.
B) Short partition processing times are indicative of low skew.
C) Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.
D) No data is exchanged between executors when coalesce() is run.
E) The coalesce() method should be used to increase the number of partitions.

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

3. Which of the following code blocks returns approximately 1000 rows, some of them potentially being duplicates, from the 2000-row DataFrame transactionsDf that only has unique rows?

A) transactionsDf.take(1000).distinct()
B) transactionsDf.sample(False, 0.5)
C) transactionsDf.sample(True, 0.5)
D) transactionsDf.take(1000)
E) transactionsDf.sample(True, 0.5, force=True)

4. Which of the following statements about RDDs is incorrect?

A) The high-level DataFrame API is built on top of the low-level RDD API.
B) RDD stands for Resilient Distributed Dataset.
C) RDDs are immutable.
D) RDDs are great for precisely instructing Spark on how to do a query.
E) An RDD consists of a single partition.

5. Which of the following is one of the big performance advantages that Spark has over Hadoop?

A) Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.
B) Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.
C) Spark achieves great performance by storing data and performing computation in memory, whereas large jobs in Hadoop require a large amount of relatively slow disk I/O operations.
D) Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes.
E) Spark achieves performance gains for developers by extending Hadoop's DataFrames with a user-friendly API.

問題與答案：