Databricks Developer for Apache Spark - Scala Certification Sample Questions

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Databricks Developer for Apache Spark - Scala Sample Questions:

01. Spark dynamically handles skew in sort-merge join by splitting (and replicating if needed) skewed partitions. Which property needs to be enabled to achieve this?

a) spark.sql.adaptive.skewJoin.enabled

b) spark.sql.skewJoin.enabled

c) spark.sql.adaptive.skewJoin.enable

d) spark.sql.adaptive.optimeze.skewJoin

02. At which stage Catalyst optimizer generates one or more physical plans?

a) Code Generation

b) Logical Optimization

c) Physical Planning

d) Analysis

03. Which of the following three operations are classified as a wide transformation?

(Choose 3 answers)

a) drop()

b) repartition()

c) filter()

d) flatMap()

e) orderBy()

f) selectDistinct()

04. At which stage do the first set of optimizations take place?

a) Physical Planning

b) Code Generation

c) Logical Optimization

d) Analysis

05. Which of the followings is NOT a useful use cases of spark?

a) Performing ad hoc or interactive queries to explore and visualize data sets

b) Building, training, and evaluating machine learning models using MLlib

c) Analyzing graph data sets and social networks

d) Processing in parallel small data sets distributed across a cluster

06. If spark is running in cluster mode, which of the following statements about nodes is incorrect?

a) Each executor is running in a JVM inside of a worker node

b) There might be more executors than total number of nodes

c) There is at least one worker node in the cluster

d) There is one single worker node that contains the Spark driver and the executors

e) The spark driver runs in its own non-worker node without any executors

07. What command can we use to get the number of partition of a dataframe name df?

a) df.rdd.getPartitionSize()

b) df.rdd.getNumPartitions()

c) df.getNumPartitions()

d) df.getPartitionSize()

08. Your application on production is crashing lately and your application gets stuck at the same level every time you restart the spark job . You know that it is the toLocalIterator function is causing the problem.
What are the possible solutions to this problem?

a) There is nothing to worry, application crashes are expected and will not affect your application at all.

b) Use collect function instead of to localIterator

c) Reduce the memory of the driver

d) Reduce the size of your partitions if possible.

09. How to make sure that dataframe df has 12 partitions given that df has 4 partitions?

a) df.setPartitition(12)

b) df.repartition(12)

c) df.repartition()

d) df.setPartitition()

10. Which of the following statements about the Spark driver is incorrect?

a) The Spark driver is horizontally scaled to increase overall processing throughput.

b) The Spark driver contains the SparkContext object.

c) The Spark driver is responsible for scheduling the execution of data by various worker nodes in cluster mode.

d) The Spark driver is the node in which the Spark application's main method runs to coordinate the Spark application.

e) The Spark driver should be as close as possible to worker nodes for optimal performance.

Answers:

Question: 01 Answer: a	Question: 02 Answer: c	Question: 03 Answer: b, e, f	Question: 04 Answer: c	Question: 05 Answer: d
Question: 06 Answer: e	Question: 07 Answer: b	Question: 08 Answer: d	Question: 09 Answer: b	Question: 10 Answer: a

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