Free Databricks Certified Associate Developer for Apache Spark 3.0 Exam braindumps download (Databricks Certified Associate Developer for Apache Spark 3.0 exam dumps Free)

QUESTION: 66

Which of the following statements about DAGs is correct?

DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.
DAG stands for "Directing Acyclic Graph".
Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.
In contrast to transformations, DAGs are never lazily executed.
DAGs can be decomposed into tasks that are executed in parallel.

Answer(s): E

Explanation:

DAG stands for "Directing Acyclic Graph". No, DAG stands for "Directed Acyclic Graph".
Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.
No, quite the opposite. You can access DAGs through the Spark UI and they can be of great help when optimizing queries manually.
In contrast to transformations, DAGs are never lazily executed.
DAGs represent the execution plan in Spark and as such are lazily executed when the driver requests the data processed in the DAG.

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QUESTION: 67

Which of the following DataFrame methods is classified as a transformation?

DataFrame.count()
DataFrame.show()
DataFrame.select()
DataFrame.foreach()
DataFrame.first()

Answer(s): C

Explanation:

DataFrame.select()
Correct, DataFrame.select() is a transformation. When the command is executed, it is evaluated lazily and returns an RDD when it is triggered by an action.
DataFrame.foreach()
Incorrect, DataFrame.foreach() is not a transformation, but an action. The intention of foreach() is to apply code to each element of a DataFrame to update accumulator variables or write the elements to external storage. The process does not return an RDD - it is an action! DataFrame.first()
Wrong. As an action, DataFrame.first() executed immediately and returns the first row of a DataFrame.
DataFrame.count()
Incorrect. DataFrame.count() is an action and returns the number of rows in a DataFrame. DataFrame.show()
No, DataFrame.show() is an action and displays the DataFrame upon execution of the command.

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QUESTION: 68

Which of the following statements about lazy evaluation is incorrect?

Predicate pushdown is a feature resulting from lazy evaluation.
Execution is triggered by transformations.
Spark will fail a job only during execution, but not during definition.
Accumulators do not change the lazy evaluation model of Spark.
Lineages allow Spark to coalesce transformations into stages

Answer(s): B

Explanation:

Execution is triggered by transformations.
Correct. Execution is triggered by actions only, not by transformations. Lineages allow Spark to coalesce transformations into stages.
Incorrect. In Spark, lineage means a recording of transformations. This lineage enables lazy evaluation in Spark.
Predicate pushdown is a feature resulting from lazy evaluation.
Wrong. Predicate pushdown means that, for example, Spark will execute filters as early in the process as possible so that it deals with the least possible amount of data in subsequent transformations, resulting in a performance improvements. Accumulators do not change the lazy evaluation model of Spark.
Incorrect. In Spark, accumulators are only updated when the query that refers to the is actually executed. In other words, they are not updated if the query is not (yet) executed due to lazy evaluation.
Spark will fail a job only during execution, but not during definition.
Wrong. During definition, due to lazy evaluation, the job is not executed and thus certain errors, for example reading from a non-existing file, cannot be caught. To be caught, the job needs to be executed, for example through an action.

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QUESTION: 69

Which of the following describes how Spark achieves fault tolerance?

Spark helps fast recovery of data in case of a worker fault by providing the MEMORY_AND_DISK storage level option.
If an executor on a worker node fails while calculating an RDD, that RDD can be recomputed by another executor using the lineage.
Spark builds a fault-tolerant layer on top of the legacy RDD data system, which by itself is not fault tolerant.
Due to the mutability of DataFrames after transformations, Spark reproduces them using observed lineage in case of worker node failure.
Spark is only fault-tolerant if this feature is specifically enabled via the spark.fault_recovery.enabled property.

Answer(s): B

Explanation:

Due to the mutability of DataFrames after transformations, Spark reproduces them using observed lineage in case of worker node failure.
Wrong – Between transformations, DataFrames are immutable. Given that Spark also records the lineage, Spark can reproduce any DataFrame in case of failure. These two aspects are the key to understanding fault tolerance in Spark.
Spark builds a fault-tolerant layer on top of the legacy RDD data system, which by itself is not fault tolerant.
Wrong. RDD stands for Resilient Distributed Dataset and it is at the core of Spark and not a "legacy system". It is fault-tolerant by design.
Spark helps fast recovery of data in case of a worker fault by providing the MEMORY_AND_DISK storage level option.
This is not true. For supporting recovery in case of worker failures, Spark provides "_2", "_3", and so on, storage level options, for example MEMORY_AND_DISK_2. These storage levels are specifically designed to keep duplicates of the data on multiple nodes. This saves time in case of a worker fault, since a copy of the data can be used immediately, vs. having to recompute it first.
Spark is only fault-tolerant if this feature is specifically enabled via the spark.fault_recovery.enabled property.
No, Spark is fault-tolerant by design.

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