Q & A
Which of the following statements about the Spark driver is incorrect?
A. The Spark driver is the node in which the Spark application’s main method runs to coordinate the Spark application.
B. The Spark driver is horizontally scaled to increase overall processing throughput.
C. The Spark driver contains the SparkContext object.
D. The Spark driver is responsible for scheduling the execution of data by various worker nodes in cluster mode.
E. The Spark driver should be as close as possible to worker nodes for optimal performance.
Answer: B. The Spark driver is horizontally scaled to increase overall processing throughput.
Explanation:
- A is correct because the Spark driver indeed runs the application’s main method and coordinates the application.
- B is incorrect because the Spark driver is a single entity and is not horizontally scalable. The driver manages and coordinates tasks but does not scale horizontally to increase throughput.
- C is correct because the Spark driver contains the SparkContext object, which is the entry point for Spark functionality.
- D is correct because the Spark driver schedules the execution of tasks on worker nodes.
- E is correct because the driver should be close to the worker nodes to minimize latency and improve performance.
Which of the following describes nodes in cluster-mode Spark?
A. Nodes are the most granular level of execution in the Spark execution hierarchy.
B. There is only one node and it hosts both the driver and executors.
C. Nodes are another term for executors, so they are processing engine instances for performing computations.
D. There are driver nodes and worker nodes, both of which can scale horizontally.
E. Worker nodes are machines that host the executors responsible for the execution of tasks.
Answer: E. Worker nodes are machines that host the executors responsible for the execution of tasks.
Explanation:
- A is incorrect because tasks, not nodes, are the most granular level of execution.
- B is incorrect because in a cluster mode, there are multiple nodes, including separate driver and worker nodes.
- C is incorrect because nodes and executors are not the same; nodes host executors.
- D is incorrect because typically, only worker nodes scale horizontally, not driver nodes.
- E is correct because worker nodes are indeed machines that host executors, which execute the tasks assigned by the driver.
Which of the following statements about slots is true? A. There must be more slots than executors.
B. There must be more tasks than slots.
C. Slots are the most granular level of execution in the Spark execution hierarchy.
D. Slots are not used in cluster mode.
E. Slots are resources for parallelization within a Spark application.
Answer: E. Slots are resources for parallelization within a Spark application.
Explanation:
- A is incorrect because there is no requirement for having more slots than executors.
- B is incorrect because the number of tasks does not necessarily have to exceed the number of slots.
- C is incorrect because tasks, not slots, are the most granular level of execution.
- D is incorrect because slots are indeed used in cluster mode to enable parallel task execution.
- E is correct because slots are resources that allow tasks to run in parallel, providing the means for concurrent execution.
Which of the following is a combination of a block of data and a set of transformers that will run on a single executor? A. Executor
B. Node
C. Job
D. Task
E. Slot
Answer: D. Task
Explanation:
- A is incorrect because an executor is a process running on a worker node that executes tasks.
- B is incorrect because a node can host multiple executors.
- C is incorrect because a job is a higher-level construct encompassing multiple stages and tasks.
- D is correct because a task is the unit of work that includes a block of data and the computation to be performed on it.
- E is incorrect because a slot is a resource for parallel task execution, not the unit of work itself.
Which of the following is a group of tasks that can be executed in parallel to compute the same set of operations on potentially multiple machines? A. Job
B. Slot
C. Executor
D. Task
E. Stage
Answer: E. Stage
Explanation:
- A is incorrect because a job is an entire computation consisting of multiple stages.
- B is incorrect because a slot is a resource for parallel task execution.
- C is incorrect because an executor runs tasks on worker nodes.
- D is incorrect because a task is a single unit of work.
- E is correct because a stage is a set of tasks that can be executed in parallel to perform the same computation.
Which of the following describes a shuffle?
A. A shuffle is the process by which data is compared across partitions.
B. A shuffle is the process by which data is compared across executors.
C. A shuffle is the process by which partitions are allocated to tasks.
D. A shuffle is the process by which partitions are ordered for write.
E. A shuffle is the process by which tasks are ordered for execution.
Answer: A. A shuffle is the process by which data is compared across partitions.
Explanation:
- A is correct because shuffling involves redistributing data across partitions to align with the needs of downstream transformations.
- B is incorrect because shuffling happens across partitions, not specifically executors.
- C is incorrect because partition allocation to tasks is not the definition of a shuffle.
- D is incorrect because shuffling is not about ordering partitions for write operations.
- E is incorrect because shuffling does not involve ordering tasks for execution.
DataFrame df is very large with a large number of partitions, more than there are executors in the cluster. Based on this situation, which of the following is incorrect? Assume there is one core per executor.
A. Performance will be suboptimal because not all executors will be utilized at the same time.
B. Performance will be suboptimal because not all data can be processed at the same time.
C. There will be a large number of shuffle connections performed on DataFrame df when operations inducing a shuffle are called.
D. There will be a lot of overhead associated with managing resources for data processing within each task.
E. There might be risk of out-of-memory errors depending on the size of the executors in the cluster.
Answer: A. Performance will be suboptimal because not all executors will be utilized at the same time.
Explanation:
- A is incorrect because having more partitions than executors does not necessarily mean executors will be underutilized; they will process partitions sequentially.
- B is correct because more partitions than executors mean data processing cannot happen all at once, affecting performance.
- C is correct because a high number of partitions can lead to many shuffle operations.
- D is correct because managing many tasks increases overhead.
- E is correct because large data volumes can risk out-of-memory errors if executor memory is insufficient.
Which of the following operations will trigger evaluation?
A. DataFrame.filter()
B. DataFrame.distinct()
C. DataFrame.intersect()
D. DataFrame.join()
E. DataFrame.count()
Answer: E. DataFrame.count()
Explanation:
- A is incorrect because
DataFrame.filter()is a transformation, which defines a computation but does not trigger it. - B is incorrect because
DataFrame.distinct()is also a transformation. - C is incorrect because
DataFrame.intersect()is a transformation. - D is incorrect because
DataFrame.join()is a transformation. - E is correct because
DataFrame.count()is an action that triggers the actual execution of the computation.
Which of the following describes the difference between transformations and actions?
A. Transformations work on DataFrames/Datasets while actions are reserved for native language objects.
B. There is no difference between actions and transformations.
C. Actions are business logic operations that do not induce execution while transformations are execution triggers focused on returning results.
D. Actions work on DataFrames/Datasets while transformations are reserved for native language objects.
E. Transformations are business logic operations that do not induce execution while actions are execution triggers focused on returning results.
Answer: E. Transformations are business logic operations that do not induce execution while actions are execution triggers focused on returning results.
Explanation:
- A is incorrect because both transformations and actions work on DataFrames/Datasets.
- B is incorrect because there is a clear difference between transformations and actions.
- C is incorrect because it incorrectly describes the role of transformations and actions.
- D is incorrect because both transformations and actions work on DataFrames/Datasets.
- E is correct because transformations define the computations without triggering them, while actions trigger the execution and return the results.