Spark Test Assessment

Candidates Assessed

12378+

Organisations Served

117+

iMocha's Spark test is the most preferred skills assessment to gauge a candidate’s knowledge of Apache Spark skills. This Spark assessment enables hiring managers and recruiters to hire job-fit candidates for a Big Data Software Engineer position. The Online Spark test can reduce hiring time by 45% and reduce technical screening time by 80%

Test Summary

Spark test enables employers and recruiters to identify potential hires by evaluating working skills and job readiness. For this reason, the emphasis is laid upon evaluating the knowledge of applied skills gained through real work experience, rather than theoretical knowledge.
Using powerful reporting, you will have a detailed analysis of test results to help you make better decisions and predict candidate and employee success.

The Spark assessment may contain MCQ's (Multiple Choice Questions), MAQ's (Multiple Answer Questions), Fill in the Blanks, Descriptive, Whiteboard Questions, Audio / Video Questions, AI-LogicBox (Pseudo-Coding Platform), Coding Simulations, True or False Questions, etc.

Test Duration: 35 minutes

No. of Questions: 12

Level of Expertise: Entry/Mid/Senior

Useful for hiring

Big Data Software Engineer - Spark
Software Developer - Spark

Topics Covered

Spark Core

Our Spark Test Assessment helps in assessment of candidate's ability of working with Spark SQL module used for structured data processing

Spark SQL

This Spark Online test evaluates candidate’s understanding about Spark structured data processing model

Spark Streaming

iMocha’s Spark assessment test helps recruiters and hiring managers to assess the candidate’s knowledge about Spark API which is used by data engineers to process real time data from different sources

Sample Questions

Choose from our 100,000+ question library or add your own questions to make powerful custom tests

Question types:

Multiple Answer

Topic:

Difficulty:

Easy

Q 1. You can cache the data from SchemaRDD by:

Note: There can be multiple correct answers to this question.

hiveCtx.cacheTable("tableName")

Hive SQL Statment CACHE TABLE tableName

regular cache method on RDDs

None of the mentioned

Question types:

Multiple Option

Topic:

Difficulty:

Easy

Q 2. Assume that we have input data below:

1;bat man;23
112;spider man;23
112;spider man;27
112;spider man;29
12;superman ;23

What is the output if we run this code?

public static void main(String[] args) {
    SparkConf conf = new SparkConf().setAppName("org.sparkexample.sampleCode").setMaster("local");
    JavaSparkContext sc = new JavaSparkContext(conf);
    JavaRDD lines = sc.textFile("C:\\Users\\Hoang Ngo\\workspace\\HelloSpark-Java\\sampledata");
    JavaPairRDD lineLengths = lines.map(s -> s.split(";", -1))
    .filter(s->!s[0].equals("1"))
    .mapToPair(s-> new Tuple2(s[0], s[0]+";"+s[1]+";"+s[2]));
    JavaPairRDD counts = lineLengths.reduceByKey((a, b) -> a+b);
    System.out.println(counts.collect());
}

[]

[(112,112;spider man;23112;spider man;27112;spider man;29)]

Run time error

[(112,112;spider man;23112;spider man;27112;spider man;29), (12,12;superman ;23)]