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Cloudera CCA175 Prüfungsübersicht:

Zertifizierungsanbieter:	Cloudera
Prüfungsname:	Prüfung zum CCA Spark- und Hadoop-Entwickler
Prüfungsnummer:	CCA175
Prüfungsgebühr:	USD 295
Verfügbare Sprachen:	Englisch
Prüfungsdauer:	120 Minuten
Gültigkeitsdauer des Zertifikats:	2 Jahre
Mindestpunktzahl:	70 %
Verwandte Zertifizierungen:	CCA Data Analyst CCP Data Engineer
Anzahl der Fragen:	8–12
Prüfungsformat:	Praxisorientiert, Praktische Aufgaben, Arbeitsumgebung mit aktivem Cluster
Empfohlenes Training:	Cloudera-Schulung für Entwickler zu Spark und Hadoop
Prüfungsanmeldung:	Cloudera-Zertifizierungsportal
Beispielfragen:
Prüfungsmethode:	Online-Prüfung mit Aufsicht, durchgeführt aus der Ferne über einen sicheren Browser
Voraussetzungen:	Keine formalen Teilnahmevoraussetzungen; praktische Erfahrung mit Scala/Python, HDFS, Hive, Sqoop, Flume und Spark wird empfohlen
Offizielle Syllabus-URL:	https://www.cloudera.com/about/training/certification/cdhhdp-certification/cca-spark.html

Cloudera CCA175 Prüfungsthemen:

Fragen und Antworten rund um die CCA175 Prüfung von Cloudera

Cloudera CCA Spark and Hadoop Developer CCA175 Prüfungsfragen mit Lösungen

Frage #1

CORRECT TEXT
Problem Scenario 8 : You have been given following mysql database details as well as other info.
Please accomplish following.
1. Import joined result of orders and order_items table join on orders.order_id = order_items.order_item_order_id.
2 . Also make sure each tables file is partitioned in 2 files e.g. part-00000, part-00002
3 . Also make sure you use orderid columns for sqoop to use for boundary conditions.

Frage #2

CORRECT TEXT
Problem Scenario 50 : You have been given below code snippet (calculating an average score}, with intermediate output.
type ScoreCollector = (Int, Double)
type PersonScores = (String, (Int, Double))
val initialScores = Array(("Fred", 88.0), ("Fred", 95.0), ("Fred", 91.0), ("Wilma", 93.0),
("Wilma", 95.0), ("Wilma", 98.0))
val wilmaAndFredScores = sc.parallelize(initialScores).cache()
val scores = wilmaAndFredScores.combineByKey(createScoreCombiner, scoreCombiner, scoreMerger) val averagingFunction = (personScore: PersonScores) => { val (name, (numberScores, totalScore)) = personScore (name, totalScore / numberScores)
}
val averageScores = scores.collectAsMap(}.map(averagingFunction)
Expected output: averageScores: scala.collection.Map[String,Double] = Map(Fred ->
91.33333333333333, Wilma -> 95.33333333333333)
Define all three required function , which are input for combineByKey method, e.g.
(createScoreCombiner, scoreCombiner, scoreMerger). And help us producing required results.

Frage #3

CORRECT TEXT
Problem Scenario 76 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of order table : (orderid , order_date , ordercustomerid, order_status}
.....
Please accomplish following activities.
1 . Copy "retail_db.orders" table to hdfs in a directory p91_orders.
2 . Once data is copied to hdfs, using pyspark calculate the number of order for each status.
3 . Use all the following methods to calculate the number of order for each status. (You need to know all these functions and its behavior for real exam)
- countByKey()
-groupByKey()
- reduceByKey()
-aggregateByKey()
- combineByKey()

Frage #4

CORRECT TEXT
Problem Scenario 48 : You have been given below Python code snippet, with intermediate output.
We want to take a list of records about people and then we want to sum up their ages and count them.
So for this example the type in the RDD will be a Dictionary in the format of {name: NAME, age:AGE, gender:GENDER}.
The result type will be a tuple that looks like so (Sum of Ages, Count) people = [] people.append({'name':'Amit', 'age':45,'gender':'M'}) people.append({'name':'Ganga', 'age':43,'gender':'F'})
people.append({'name':'John', 'age':28,'gender':'M'})
people.append({'name':'Lolita', 'age':33,'gender':'F'})
people.append({'name':'Dont Know', 'age':18,'gender':'T'})
peopleRdd=sc.parallelize(people) //Create an RDD
peopleRdd.aggregate((0,0), seqOp, combOp) //Output of above line : 167, 5)
Now define two operation seqOp and combOp , such that
seqOp : Sum the age of all people as well count them, in each partition. combOp :
Combine results from all partitions.

Frage #5

CORRECT TEXT
Problem Scenario 20 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.categories
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
1. Write a Sqoop Job which will import "retaildb.categories" table to hdfs, in a directory name "categories_targetJob".

Fragen und Antworten: