Databricks Certified Associate Developer for Apache Spark - Certified Associate Developer for Apache Spark Exam
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Total 342 questions
Question #11 (Topic: Exam A)
Which of the following describes the difference between cluster and client execution modes?
A. The cluster execution mode runs the driver on a worker node within a cluster, while the client execution mode runs the driver on the client machine (also known as a gateway machine or edge node).
B. The cluster execution mode is run on a local cluster, while the client execution mode is run in the cloud.
C. The cluster execution mode distributes executors across worker nodes in a cluster, while the client execution mode runs a Spark job entirely on one client machine.
D. The cluster execution mode runs the driver on the cluster machine (also known as a gateway machine or edge node), while the client execution mode runs the driver on a worker node within a cluster.
E. The cluster execution mode distributes executors across worker nodes in a cluster, while the client execution mode submits a Spark job from a remote machine to be run on a remote, unconfigurable cluster.
Answer: A
Question #12 (Topic: Exam A)
Which of the following statements about Spark’s stability is incorrect?
A. Spark is designed to support the loss of any set of worker nodes.
B. Spark will rerun any failed tasks due to failed worker nodes.
C. Spark will recompute data cached on failed worker nodes.
D. Spark will spill data to disk if it does not fit in memory.
E. Spark will reassign the driver to a worker node if the driver’s node fails.
Answer: E
Question #13 (Topic: Exam A)
Which of the following cluster configurations is most likely to experience an out-of-memory error in response to data skew in a single partition?
Note: each configuration has roughly the same compute power using 100 GB of RAM and 200 cores.
Note: each configuration has roughly the same compute power using 100 GB of RAM and 200 cores.
A. Scenario #4
B. Scenario #5
C. Scenario #6
D. More information is needed to determine an answer.
E. Scenario #1
Answer: C
Question #14 (Topic: Exam A)
Of the following situations, in which will it be most advantageous to store DataFrame df at the MEMORY_AND_DISK storage level rather than the MEMORY_ONLY storage level?
A. When all of the computed data in DataFrame df can fit into memory.
B. When the memory is full and it’s faster to recompute all the data in DataFrame df rather than read it from disk.
C. When it’s faster to recompute all the data in DataFrame df that cannot fit into memory based on its logical plan rather than read it from disk.
D. When it’s faster to read all the computed data in DataFrame df that cannot fit into memory from disk rather than recompute it based on its logical plan.
E. The storage level MENORY_ONLY will always be more advantageous because it’s faster to read data from memory than it is to read data from disk.
Answer: D
Question #15 (Topic: Exam A)
A Spark application has a 128 GB DataFrame A and a 1 GB DataFrame B. If a broadcast join were to be performed on these two DataFrames, which of the following describes which DataFrame should be broadcasted and why?
A. Either DataFrame can be broadcasted. Their results will be identical in result and efficiency.
B. DataFrame B should be broadcasted because it is smaller and will eliminate the need for the shuffling of itself.
C. DataFrame A should be broadcasted because it is larger and will eliminate the need for the shuffling of DataFrame B.
D. DataFrame B should be broadcasted because it is smaller and will eliminate the need for the shuffling of DataFrame A.
E. DataFrame A should be broadcasted because it is smaller and will eliminate the need for the shuffling of itself.
Answer: D
