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NEW QUESTION 53
The code block displayed below contains an error. The code block should trigger Spark to cache DataFrame transactionsDf in executor memory where available, writing to disk where insufficient executor memory is available, in a fault-tolerant way. Find the error.
Code block:
transactionsDf.persist(StorageLevel.MEMORY_AND_DISK)

  • A. Data caching capabilities can be accessed through the spark object, but not through the DataFrame API.
  • B. The storage level is inappropriate for fault-tolerant storage.
  • C. The code block uses the wrong operator for caching.
  • D. The DataFrameWriter needs to be invoked.
  • E. Caching is not supported in Spark, data are always recomputed.

Answer: B

Explanation:
Explanation
The storage level is inappropriate for fault-tolerant storage.
Correct. Typically, when thinking about fault tolerance and storage levels, you would want to store redundant copies of the dataset. This can be achieved by using a storage level such as StorageLevel.MEMORY_AND_DISK_2.
The code block uses the wrong command for caching.
Wrong. In this case, DataFrame.persist() needs to be used, since this operator supports passing a storage level.
DataFrame.cache() does not support passing a storage level.
Caching is not supported in Spark, data are always recomputed.
Incorrect. Caching is an important component of Spark, since it can help to accelerate Spark programs to great extent. Caching is often a good idea for datasets that need to be accessed repeatedly.
Data caching capabilities can be accessed through the spark object, but not through the DataFrame API.
No. Caching is either accessed through DataFrame.cache() or DataFrame.persist().
The DataFrameWriter needs to be invoked.
Wrong. The DataFrameWriter can be accessed via DataFrame.write and is used to write data to external data stores, mostly on disk. Here, we find keywords such as "cache" and "executor memory" that point us away from using external data stores. We aim to save data to memory to accelerate the reading process, since reading from disk is comparatively slower. The DataFrameWriter does not write to memory, so we cannot use it here.
More info: Best practices for caching in Spark SQL | by David Vrba | Towards Data Science

 

NEW QUESTION 54
Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

  • A. There must be less executors than tasks.
  • B. There must be more slots than tasks.
  • C. Tasks run in parallel via slots.
  • D. An executor runs on a single core.
  • E. Slot is another name for executor.

Answer: C

Explanation:
Explanation
Tasks run in parallel via slots.
Correct. Given the assumption, an executor then has one or more "slots", defined by the equation spark.executor.cores / spark.task.cpus. With the executor's resources divided into slots, each task takes up a slot and multiple tasks can be executed in parallel.
Slot is another name for executor.
No, a slot is part of an executor.
An executor runs on a single core.
No, an executor can occupy multiple cores. This is set by the spark.executor.cores option.
There must be more slots than tasks.
No. Slots just process tasks. One could imagine a scenario where there was just a single slot for multiple tasks, processing one task at a time. Granted - this is the opposite of what Spark should be used for, which is distributed data processing over multiple cores and machines, performing many tasks in parallel.
There must be less executors than tasks.
No, there is no such requirement.
More info: Spark Architecture | Distributed Systems Architecture (https://bit.ly/3x4MZZt)

 

NEW QUESTION 55
Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?

  • A. spark.read().path(filePath)
  • B. spark.read.path(filePath, source="json")
  • C. spark.read.json(filePath)
  • D. spark.read.path(filePath)
  • E. spark.read().json(filePath)

Answer: C

Explanation:
Explanation
spark.read.json(filePath)
Correct. spark.read accesses Spark's DataFrameReader. Then, Spark identifies the file type to be read as JSON type by passing filePath into the DataFrameReader.json() method.
spark.read.path(filePath)
Incorrect. Spark's DataFrameReader does not have a path method. A universal way to read in files is provided by the DataFrameReader.load() method (link below).
spark.read.path(filePath, source="json")
Wrong. A DataFrameReader.path() method does not exist (see above).
spark.read().json(filePath)
Incorrect. spark.read is a way to access Spark's DataFrameReader. However, the DataFrameReader is not callable, so calling it via spark.read() will fail.
spark.read().path(filePath)
No, Spark's DataFrameReader is not callable (see above).
More info: pyspark.sql.DataFrameReader.json - PySpark 3.1.2 documentation, pyspark.sql.DataFrameReader.load - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

 

NEW QUESTION 56
Which of the following describes a shuffle?

  • A. A shuffle is a process that allocates partitions to executors.
  • B. A shuffle is a Spark operation that results from DataFrame.coalesce().
  • C. A shuffle is a process that compares data across partitions.
  • D. A shuffle is a process that is executed during a broadcast hash join.
  • E. A shuffle is a process that compares data across executors.

Answer: C

Explanation:
Explanation
A shuffle is a Spark operation that results from DataFrame.coalesce().
No. DataFrame.coalesce() does not result in a shuffle.
A shuffle is a process that allocates partitions to executors.
This is incorrect.
A shuffle is a process that is executed during a broadcast hash join.
No, broadcast hash joins avoid shuffles and yield performance benefits if at least one of the two tables is small in size (<= 10 MB by default). Broadcast hash joins can avoid shuffles because instead of exchanging partitions between executors, they broadcast a small table to all executors that then perform the rest of the join operation locally.
A shuffle is a process that compares data across executors.
No, in a shuffle, data is compared across partitions, and not executors.
More info: Spark Repartition & Coalesce - Explained (https://bit.ly/32KF7zS)

 

NEW QUESTION 57
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