LinkedIn Hadoop Assessment Answers 2021

LinkedIn Hadoop Assessment Answers 2021 feature is recently introduced by LinkedIn. These assessments are actually quiz-dependent on multiple-choice questions. Each assessment has around 15 questions which are taken randomly from a major pool of questions that are accessible in various categories. Passing these assessments will earn you an assessment identification that will appear alongside your skill on your LinkedIn profile. In the event that you bomb an assessment or got an underwhelming score, you can retake it after 3 months We can help you pass LinkedIn skill assessments with a high score for sure.

LinkedIn Hadoop Assessment Quiz Answers

Linkedin Hadoop Assessment Questions and Answers 2021

Q1. Partitioner controls the partitioning of what data?

final keys
final values
intermediate keys
intermediate values

Q2. SQL Windowing functions are implemented in Hive using which keywords?

UNION DISTINCT, RANK
OVER, RANK
OVER, EXCEPT
UNION DISTINCT, RANK

Q3. Rather than adding a Secondary Sort to a slow Reduce job, it is Hadoop best practice to perform which optimization?

Add a partitioned shuffle to the Map job.
Add a partitioned shuffle to the Reduce job.
Break the Reduce job into multiple, chained Reduce jobs.
Break the Reduce job into multiple, chained Map jobs.

Q4. Hadoop Auth enforces authentication on protected resources. Once authentication has been established, it sets what type of authenticating cookie?

encrypted HTTP
unsigned HTTP
compressed HTTP
signed HTTP

Q5. MapReduce jobs can be written in which language?

Java or Python
SQL only
SQL or Java
Python or SQL

Q6. To perform local aggregation of the intermediate outputs, MapReduce users can optionally specify which object?

Reducer
Combiner
Mapper
Counter

Q7. To verify job status, look for the value **\_** in the **\_**.

SUCCEEDED; syslog
SUCCEEDED; stdout
DONE; syslog
DONE; stdout

Q8. Which line of code implements a Reducer method in MapReduce 2.0?

public void reduce(Text key, Iterator values, Context context){…}
public static void reduce(Text key, IntWritable[] values, Context context){…}
public static void reduce(Text key, Iterator values, Context context){…}
public void reduce(Text key, IntWritable[] values, Context context){…}

Q9. To get the total number of mapped input records in a map job task, you should review the value of which counter?

FileInputFormatCounter
FileSystemCounter
JobCounter
TaskCounter (NOT SURE)

Q10. Hadoop Core supports which CAP capabilities?

A, P
C, A
C, P
C, A, P

Q11. What are the primary phases of a Reducer?

combine, map, and reduce
shuffle, sort, and reduce
reduce, sort, and combine
map, sort, and combine

Q12. To set up Hadoop workflow with synchronization of data between jobs that process tasks both on disk and in memory, use the _ service, which is _.

Oozie; open source
Oozie; commercial software
Zookeeper; commercial software
Zookeeper; open source

Q13. For high availability, use multiple nodes of which type?

data
name
memory
worker

Q14. DataNode supports which type of drives?

hot swappable
cold swappable
warm swappable
non-swappable

Q15. Which method is used to implement Spark jobs?

on disk of all workers
on disk of the master node
in memory of the master node
in memory of all workers

Q16. In a MapReduce job, where does the map() function run?

on the reducer nodes of the cluster
on the data nodes of the cluster (NOT SURE)
on the master node of the cluster
on every node of the cluster

Q17. To reference a master file for lookups during Mapping, what type of cache should be used?

distributed cache
local cache
partitioned cache
cluster cache

Q18. Skip bad records provide an option where a certain set of bad input records can be skipped when processing what type of data?

cache inputs
reducer inputs
intermediate values
map inputs

Related Post:

All Freelancer Platform Exam Answers

Search This Blog