Cloudera CCA175 Exam

Certification Provider: Cloudera

Exam Name: CCA Spark and Hadoop Developer

Number of questions in our database: 96

Exam Version: Nov. 23, 2023

CCA175 Exam Official Topics:

Topic 1: Understand the fundamentals of querying datasets in Spark/ Write the results back into HDFS using Spark
Topic 2: Write queries that calculate aggregate statistics/ Load data from HDFS for use in Spark applications
Topic 3: Use meta store tables as an input source or an output sink for Spark applications/ Filter data using Spark
Topic 4: Generate reports by using queries against loaded data/ Produce ranked or sorted data
Topic 5: Perform standard extract, transform, load (ETL) processes on data using the Spark API/ Join disparate datasets using Spark
Topic 6: Use Spark SQL to interact with the meta store programmatically in your applications/ Read and write files in a variety of file formats

Free Cloudera CCA175 Exam Actual Questions

The questions for CCA175 were last updated On Nov. 23, 2023

Question #1

Problem Scenario 92 : You have been given a spark scala application, which is bundled in jar named hadoopexam.jar.

Your application class name is com.hadoopexam.MyTask

You want that while submitting your application should launch a driver on one of the cluster node.

Please complete the following command to submit the application.

spark-submit XXX -master yarn \

YYY SSPARK HOME/lib/hadoopexam.jar 10

ASolution
XXX: -class com.hadoopexam.MyTask

BSolution
XXX: -class com.hadoopexam.MyTask
YYY : --deploy-mode cluster

Reveal Solution

Correct Answer: B

Question #2

Problem Scenario 95 : You have to run your Spark application on yarn with each executor Maximum heap size to be 512MB and Number of processorcores to allocate on each executor will be 1 and Your main application required three values as input arguments V1 V2 V3.

Please replace XXX, YYY, ZZZ

./bin/spark-submit -class com.hadoopexam.MyTask --master yarn-cluster--num-executors 3 --driver-memory 512m XXX YYY lib/hadoopexam.jarZZZ

ASolution
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.

BSolution
XXX: -executor-memory 510m YYY: -executor-cores 1
ZZZ : V2 V6 V1
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.

Reveal Solution

Correct Answer: A

Question #3

Problem Scenario 96 : Your spark application required extra Java options as below. -XX:+PrintGCDetails-XX:+PrintGCTimeStamps

Please replace the XXX values correctly

./bin/spark-submit --name "My app" --master local[4] --conf spark.eventLog.enabled=talse --conf XXX hadoopexam.jar

ASolution
XXX: Mspark.executoi\extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps'
Notes: ./bin/spark-submit \
--class <maln-class>
--master <master-url> \
--deploy-mode <deploy-mode> \
-conf <key>=<value> \
... # other options

\
[application-arguments]
Here, conf is used to pass the Spark related contigs which are required for the application to run like any specific property(executor memory) or if you want to override the default property which is set in Spark-default.conf.

BSolution
XXX: Mspark.executoi\extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps'
Notes: ./bin/spark-submit \

Reveal Solution

Correct Answer: A

Question #4

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()

ASolution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba -password=cloudera -table=orders --target-dir=p91_orders
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile('p91_orders')
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(',')[3], ''))
#Using countByKey, aggregate data based on status as a key output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)',')[3], 1))
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(','}[3], 1))
#Using countByKey, aggregate data based on status as a key output= keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue =
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(',')[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value)
tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)

BSolution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba -password=cloudera -table=orders --target-dir=p91_orders
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile('p91_orders')
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(',')[3], ''))
#Using countByKey, aggregate data based on status as a key output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)',')[3], 1))
#Using countByKey, aggregate data based on status as a key output= keyValue.groupByKey().map(lambda kv: (kv[0], sum(kv[1]}}}
tor line in output.collect(): print(line}
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(','}[3], 1))
#Using countByKey, aggregate data based on status as a key output= keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue = allOrders.map(lambda line: (line.split(',')[3], line}}
output=keyValue.aggregateByKey(0, lambda a, b: a+1, lambda a, b: a+b}
for line in output.collect(): print(line}
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(',')[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value)
tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)

Reveal Solution

Correct Answer: B

Question #5

Problem Scenario 94 : You have to run your Spark application on yarn with each executor 20GB and number of executors should be 50.Please replace XXX, YYY, ZZZ

export HADOOP_CONF_DIR=XXX

./bin/spark-submit \

-class com.hadoopexam.MyTask \

xxx\

-deploy-mode cluster \ # can be client for client mode

YYY\

222 \

/path/to/hadoopexam.jar \

1000

ASolution
XXX: -master yarn
YYY : -executor-memory 20G
ZZZ: -num-executors 50

BSolution
XXX: -master yarn
YYY : -executor-memory 40G
ZZZ: -num-executors 80

Reveal Solution

Correct Answer: A

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