Cleared CCA175 - 05FEB2018


#1

Hi Durga Sir & All,

I have got 8 out of 9 in the CCA175 exam. Kudos to Durga Sir! Thanks a lot for helping many like me. Learned a lot from your channel. Needless to say that I would not have been able to pass this exam without watching your videos.

Thanks

I would like to share my exam experience.

  1. 2 sqoop(import & export) questions & 7 spark.
  2. Focus more on compression techniques.
  3. Just for exam do not spend lot of time on spark streaming.
  4. Keep last 15 minutes just for checking output location and see if the format and compression are correct.
  5. size of the datasets are very small that default spark-shell options worked quick enough for me.

Finally, we should be able to answer all questions correctly if we do all output validations and follow Durga Sir instructions.


#2

Cong’s Murali… is there any questions on spark streaming???


#3

Congratulations Murali,

Can you please confirm if we need to import various packages during the exams OR are all the packages already imported?.
Eg -
import sqlContext.implicits._ (when creating sqlContext)
import org.apache.spark.sql.functions._ (when we want to execute aggregator function with dataframes)
sqlContext.setConf(“spark.sql.shuffle.partitions”, “2”) - (for restriction no of partitions)
import com.databricks.spark.avro._ (for using avro format)
etc


#4

No questions on spark streaming


#5

U need to explicitly import avro dependencies. u don’t need to import other dependencies


#6

Congrats @tmuralikrishna, quick question, have you got any Impala or Flume questions.

Thanks


#7

could u brief on the split


#8

No questions on flume/kafka/spark streaming.


#9

Hi @tmuralikrishna
How much of RDD transformation techniques were required in order to solve the problems. I am more comfortable with dataframes and sparkSQL. Can we solve all spark problems without using RDDs and just by relying on DF or SQL?
Thanks in advance.


#10

Hi,

Can you please reply whether we have to solve the problem definitely in RDD’s or we are free to choose DF’s and Spark -SQL for any problem.