Databricks Associate-Developer-Apache-Spark-3.5題庫介紹

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Databricks Associate-Developer-Apache-Spark-3.5 考試大綱主題:

最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題：

1. 16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A) The Spark engine optimizes the execution plan during the transformations, causing delays.
B) Transformations are executed immediately to build the lineage graph.
C) Transformations are evaluated lazily.
D) Only actions trigger the execution of the transformation pipeline.
E) The Spark engine requires manual intervention to start executing transformations.

2. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option checkpointLocation during writeStream
B) By configuring the option recoveryLocation during the SparkSession initialization
C) By configuring the option checkpointLocation during readStream
D) By configuring the option recoveryLocation during writeStream

3. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It runs on a single node only, utilizing memory efficiently.
B) It computes results immediately using eager execution.
C) It is available only with Python, thereby reducing the learning curve.
D) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

4. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.to_date("timestamp")).show()
B) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
C) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
D) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()

5. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
B) Set the number of partitions equal to the total number of CPUs in the cluster
C) Set the number of partitions equal to the number of nodes in the cluster
D) Set the number of partitions to a fixed value, such as 200

問題與答案：