Cloudera CCA Spark and Hadoop Developer CCA175 Exam Questions

Problem Scenario 26 : You need to implement near real time solutions for collecting information when submitted in file with below information. You have been given below directory location (if not available than create it) /tmp/nrtcontent. Assume your departments upstream service is continuously committing data in this directory as a new file (not stream of data, because it is near real time solution). As soon as file committedin this directory that needs to be available in hdfs in /tmp/flume location

Data

echo "I am preparing for CCA175 from ABCTECH.com" > /tmp/nrtcontent/.he1.txt

mv /tmp/nrtcontent/.he1.txt /tmp/nrtcontent/he1.txt

After few mins

echo "I am preparing for CCA175 from TopTech.com" > /tmp/nrtcontent/.qt1.txt

mv /tmp/nrtcontent/.qt1.txt /tmp/nrtcontent/qt1.txt

Write a flume configuration file named flumes.conf and use it to load data in hdfs with following additional properties.

1. Spool /tmp/nrtcontent

2. File prefix in hdfs sholuld be events

3. File suffix should be Jog

4. If file is not commited and in use than it should have as prefix.

5. Data should be written as text to hdfs

Problem Scenario 46 : You have been given belwo list in scala (name,sex,cost) for each work done.

List( ("Deeapak" , "male", 4000), ("Deepak" , "male", 2000), ("Deepika" , "female", 2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000) , ("Neeta" , "female", 2000))

Now write a Spark program to load this list as an RDD and do the sum of cost for combination of name and sex (as key)

Problem Scenario 21 : You have been given log generating service as below.

startjogs (It will generate continuous logs)

tailjogs (You can check , what logs are being generated)

stopjogs (It will stop the log service)

Path where logs are generated using above service : /opt/gen_logs/logs/access.log

Now write a flume configuration file named flumel.conf , using that configuration file dumps logs in HDFS file system in a directory called flumel. Flume channel should have following property as well. After every 100 message it should be committed, use non-durable/faster channel and it should be able to hold maximum 1000 events

Problem Scenario 7 : You have been given following mysql database details as well as other info.

user=retail_dba

password=cloudera

database=retail_db

jdbc URL = jdbc:mysql://quickstart:3306/retail_db

Please accomplish following.

1. Import department tables using your custom boundary query, which import departments between 1 to 25.

2. Also make sure each tables file is partitioned in 2 files e.g. part-00000, part-00002

3. Also make sure you have imported only two columns from table, which are department_id,department_name

Problem Scenario 16 : You have been given following mysql database details as well as other info.

user=retail_dba

password=cloudera

database=retail_db

jdbc URL = jdbc:mysql://quickstart:3306/retail_db

Please accomplish below assignment.

1. Create a table in hive as below.

create table departments_hive(department_id int, department_name string);

2. Now import data from mysql table departments to this hive table. Please make sure that data should be visible using below hive command, select" from departments_hive

Problem Scenario 73 : You have been given data in json format as below.

{"first_name":"Ankit", "last_name":"Jain"}

{"first_name":"Amir", "last_name":"Khan"}

{"first_name":"Rajesh", "last_name":"Khanna"}

{"first_name":"Priynka", "last_name":"Chopra"}

{"first_name":"Kareena", "last_name":"Kapoor"}

{"first_name":"Lokesh", "last_name":"Yadav"}

Do the following activity

1. create employee.json file locally.

2. Load this file on hdfs

3. Register this data as a temp table in Spark using Python.

4. Write select query and print this data.

5. Now save back this selected data in json format.

Problem Scenario 38 : You have been given an RDD as below,

val rdd: RDD[Array[Byte]]

Now you have to save this RDD as a SequenceFile. And below is the code snippet.

import org.apache.hadoop.io.compress.GzipCodec

rdd.map(bytesArray => (A.get(), new B(bytesArray))).saveAsSequenceFile('7output/path",classOt[GzipCodec])

What would be the correct replacement for A and B in above snippet.