Cloudera CCA Spark and Hadoop Developer : CCA175 valid dump

Correct Answer:

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create an RDD out of this list
val rdd = sc.parallelize(List( ("Deeapak" , "male", 4000}, ("Deepak" , "male", 2000),
("Deepika" , "female", 2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000} ,
("Neeta" , "female", 2000}}}
Step 2 : Convert this RDD in pair RDD
val byKey = rdd.map({case (name,sex,cost) => (name,sex)->cost})
Step 3 : Now group by Key
val byKeyGrouped = byKey.groupByKey
Step 4 : Nowsum the cost for each group
val result = byKeyGrouped.map{case ((id1,id2),values) => (id1,id2,values.sum)}
Step 5 : Save the results result.repartition(1).saveAsTextFile("spark12/result.txt")

Correct Answer:

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Select Maximum, minimum, average , Standard Deviation, and total quantity.
val results = sqlContext.sql('.....SELECT MAX(price) AS MAX , MIN(price) AS MIN ,
AVG(price) AS Average, STD(price) AS STD, SUM(quantity) AS total_products FROM products......) results. showQ
Step 2 : Select minimum and maximum price for each product code.
val results = sqlContext.sql(......SELECT code, MAX(price) AS Highest Price', MIN(price)
AS Lowest Price'
FROM products GROUP BY code......)
results. showQ
Step 3 : Select Maximum, minimum, average , Standard Deviation, and total quantity for each product code, hwoever make sure Average and Standard deviation will have maximum two decimal values.
val results = sqlContext.sql(......SELECT code, MAX(price), MIN(price),
CAST(AVG(price} AS DECIMAL(7,2)) AS Average', CAST(STD(price) AS DECIMAL(7,2))
AS 'Std Dev\ SUM(quantity) FROM products
GROUP BY code......)
results. showQ
Step 4 : Select all the product code and average price only where product count is more than or equal to 3.
val results = sqlContext.sql(......SELECT code AS Product Code',
COUNTf) AS Count',
CAST(AVG(price) AS DECIMAL(7,2)) AS Average' FROM products GROUP BY code
HAVING Count >=3"M") results. showQ
Step 5 : Select maximum, minimum , average and total of all the products for each code.
Also produce the same across all the products.
val results = sqlContext.sql( """SELECT
code,
MAX(price),
MIN(pnce),
CAST(AVG(price) AS DECIMAL(7,2)) AS Average',
SUM(quantity)-
FROM products
GROUP BY code
WITH ROLLUP""" )
results. show()

Correct Answer:

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create an application with following code and store it in problem84.py
# Import SparkContext and SparkConf
from pyspark import SparkContext, SparkConf
# Create configuration object and set App name
conf = SparkConf().setAppName("CCA 175 Problem 84") sc = sparkContext(conf=conf)
#load data from hdfs
contentRDD = sc.textFile(MContent.txt")
#filter out non-empty lines
nonemptyjines = contentRDD.filter(lambda x: len(x) > 0)
#Split line based on space
words = nonempty_lines.ffatMap(lambda x: x.split(''}}
#filter out all 2 letter words
finalRDD = words.filter(lambda x: len(x) > 2)
for word in finalRDD.collect():
print(word)
#Save final data finalRDD.saveAsTextFile("problem84M)
step 2 : Submit this application
spark-submit -master yarn problem84.py

Correct Answer:

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution
XXX: -executor-memory 512m YYY: -executor-cores 1
ZZZ : V1 V2 V3
Notes : spark-submit on yarn options Option Description
archives Comma-separated list of archives to be extracted into the working directory of each executor. The path must be globally visible inside your cluster; see Advanced
Dependency Management.
executor-cores Number of processor cores to allocate on each executor. Alternatively, you can use the spark.executor.cores property, executor-memory Maximum heap size to allocate to each executor. Alternatively, you can use the spark.executor.memory-property.
num-executors Total number of YARN containers to allocate for this application.
Alternatively, you can use the spark.executor.instances property. queue YARN queue to submit to. For more information, see Assigning Applications and Queries to Resource
Pools. Default: default.

Correct Answer:

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution : a.glom.collect
glom
Assembles an array that contains all elements of the partition and embeds it in an RDD.
Each returned array contains the contents of one panition