1. CCA Spark and Hadoop Developer Exam - Performance Based Scenarios Cloudera CCA175

2. CCA 175 Spark and Hadoop Developer is one of the well recognized Big Data certification. This scenario based certification exam demands basic programming using Python or Scale along with Spark and other Big Data technologies. Description

3. Required Skills  Data Ingest  Transform, Stage, and Store  Data Analysis  Configuration Get 2018 Best CCA175 Actual Test Preparation Solutions For Guaranteed Success

4. Number of Questions : 8–12 performance-based (hands- on) tasks on Cloudera Enterprise cluster. Time Limit : 120 minutes Passing Score : 70% Language : English Price : USD $295 Exam Details

5. Exam Question Format Each CCA question requires you to solve a particular scenario. In some cases, a tool such as Impala or Hive may be used. In other cases, coding is required. In order to speed up development time of Spark questions, a template may be provided that contains a skeleton of the solution, asking the candidate to fill in the missing lines with functional code. This template will either be written in Scale or written in Python, but not necessarily both. You are not required to use the template and may solve the scenario using a language you prefer. Be aware, however, that coding every problem from scratch may take more time than is allocated for the exam. Evaluation, Score Reporting, and Certificate Your exam is graded immediately upon submission and you are e-mailed a score report the same day as your exam. Your score report displays the problem number for each problem you attempted and a grade on that problem. If you fail a problem, the score report includes the criteria you failed (e.g., “Records contain incorrect data” or “Incorrect file format”). We do not report more information in order to protect the exam content. Read more about reviewing exam content on the FAQ. If you pass the exam, you receive a second e-mail within a few days of your exam with your digital certificate as a PDF, your license number, a LinkedIn profile update, and a link to download your CCA logos for use in your personal business collateral and social media profiles

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9. Questions No :1 Problem Scenario 95 : You have to run your Spark application on yarn with each executor Maximum heap size to be 512MB and Number of processor cores 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 Options 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.

10. Questions No :2 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 Answer: See the explanation for Step by Step Solution and configuration. Explanation: XXX: Mspark.executoi\extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" Notes:./bin/spark-submit \ IT Certification Guaranteed, The Easy Way! --class --master \ --deploy-mode \ -conf = \ # 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.

11. Questions No :3 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) Answer:See the explanation for Step by Step Solution and configuration. Explanation: 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")

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