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Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps
NEW QUESTION 48
Which of the following describes characteristics of the Spark UI?
Answer: C
Explanation:
Explanation
There is a place in the Spark UI that shows the property spark.executor.memory.
Correct, you can see Spark properties such as spark.executor.memory in the Environment tab.
Some of the tabs in the Spark UI are named Jobs, Stages, Storage, DAGs, Executors, and SQL.
Wrong - Jobs, Stages, Storage, Executors, and SQL are all tabs in the Spark UI. DAGs can be inspected in the
"Jobs" tab in the job details or in the Stages or SQL tab, but are not a separate tab.
Via the Spark UI, workloads can be manually distributed across distributors.
No, the Spark UI is meant for inspecting the inner workings of Spark which ultimately helps understand, debug, and optimize Spark transactions.
Via the Spark UI, stage execution speed can be modified.
No, see above.
The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.
No, there is no Scheduler tab.
NEW QUESTION 49
Which of the following is the idea behind dynamic partition pruning in Spark?
Answer: A
Explanation:
Explanation
Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.
No - this is what adaptive query execution does, but not dynamic partition pruning.
Dynamic partition pruning concatenates columns of similar data types to optimize join performance.
Wrong, this answer does not make sense, especially related to dynamic partition pruning.
Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.
It is true that dynamic partition pruning works in joins using broadcast variables. This actually happens in both the logical optimization and the physical planning stage. However, data types do not play a role for the reoptimization.
Dynamic partition pruning performs wide transformations on disk instead of in memory.
This answer does not make sense. Dynamic partition pruning is meant to accelerate Spark - performing any transformation involving disk instead of memory resources would decelerate Spark and certainly achieve the opposite effect of what dynamic partition pruning is intended for.
NEW QUESTION 50
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
Answer: D
Explanation:
Explanation
The key to solving this question is knowing (or reading in the documentation) that, by default, cache() stores values to memory and writes any partitions for which there is insufficient memory to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option listed here. It is also worth noting that cache() does not have any arguments.
If you have troubles finding the storage level information in the documentation, please also see this student Q&A thread that sheds some light here.
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 51
......
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