CCA175 Exam Question 31
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Problem Scenario 85 : In Continuation of previous question, please accomplish following activities.
1. Select all the columns from product table with output header as below. productID AS ID code AS Code name AS Description price AS 'Unit Price'
2. Select code and name both separated by ' -' and header name should be Product
Description'.
3. Select all distinct prices.
4 . Select distinct price and name combination.
5 . Select all price data sorted by both code and productID combination.
6 . count number of products.
7 . Count number of products for each code.
Problem Scenario 85 : In Continuation of previous question, please accomplish following activities.
1. Select all the columns from product table with output header as below. productID AS ID code AS Code name AS Description price AS 'Unit Price'
2. Select code and name both separated by ' -' and header name should be Product
Description'.
3. Select all distinct prices.
4 . Select distinct price and name combination.
5 . Select all price data sorted by both code and productID combination.
6 . count number of products.
7 . Count number of products for each code.
CCA175 Exam Question 32
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Problem Scenario 38 : You have been given an RDD as below,
val rdd: RDD[Array[Byte]]
Now you have to save this RDD as a SequenceFile. And below is the code snippet.
import org.apache.hadoop.io.compress.GzipCodec
rdd.map(bytesArray => (A.get(), new
B(bytesArray))).saveAsSequenceFile('7output/path",classOt[GzipCodec])
What would be the correct replacement for A and B in above snippet.
Problem Scenario 38 : You have been given an RDD as below,
val rdd: RDD[Array[Byte]]
Now you have to save this RDD as a SequenceFile. And below is the code snippet.
import org.apache.hadoop.io.compress.GzipCodec
rdd.map(bytesArray => (A.get(), new
B(bytesArray))).saveAsSequenceFile('7output/path",classOt[GzipCodec])
What would be the correct replacement for A and B in above snippet.
CCA175 Exam Question 33
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Problem Scenario 81 : You have been given MySQL DB with following details. You have been given following product.csv file product.csv productID,productCode,name,quantity,price
1001,PEN,Pen Red,5000,1.23
1002,PEN,Pen Blue,8000,1.25
1003,PEN,Pen Black,2000,1.25
1004,PEC,Pencil 2B,10000,0.48
1005,PEC,Pencil 2H,8000,0.49
1006,PEC,Pencil HB,0,9999.99
Now accomplish following activities.
1 . Create a Hive ORC table using SparkSql
2 . Load this data in Hive table.
3 . Create a Hive parquet table using SparkSQL and load data in it.
Problem Scenario 81 : You have been given MySQL DB with following details. You have been given following product.csv file product.csv productID,productCode,name,quantity,price
1001,PEN,Pen Red,5000,1.23
1002,PEN,Pen Blue,8000,1.25
1003,PEN,Pen Black,2000,1.25
1004,PEC,Pencil 2B,10000,0.48
1005,PEC,Pencil 2H,8000,0.49
1006,PEC,Pencil HB,0,9999.99
Now accomplish following activities.
1 . Create a Hive ORC table using SparkSql
2 . Load this data in Hive table.
3 . Create a Hive parquet table using SparkSQL and load data in it.
CCA175 Exam Question 34
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Problem Scenario 23 : You have been given log generating service as below.
Start_logs (It will generate continuous logs)
Tail_logs (You can check , what logs are being generated)
Stop_logs (It will stop the log service)
Path where logs are generated using above service : /opt/gen_logs/logs/access.log
Now write a flume configuration file named flume3.conf , using that configuration file dumps logs in HDFS file system in a directory called flumeflume3/%Y/%m/%d/%H/%M
Means every minute new directory should be created). Please us the interceptors to provide timestamp information, if message header does not have header info.
And also note that you have to preserve existing timestamp, if message contains it. Flume channel should have following property as well. After every 100 message it should be committed, use non-durable/faster channel and it should be able to hold maximum 1000 events.
Problem Scenario 23 : You have been given log generating service as below.
Start_logs (It will generate continuous logs)
Tail_logs (You can check , what logs are being generated)
Stop_logs (It will stop the log service)
Path where logs are generated using above service : /opt/gen_logs/logs/access.log
Now write a flume configuration file named flume3.conf , using that configuration file dumps logs in HDFS file system in a directory called flumeflume3/%Y/%m/%d/%H/%M
Means every minute new directory should be created). Please us the interceptors to provide timestamp information, if message header does not have header info.
And also note that you have to preserve existing timestamp, if message contains it. Flume channel should have following property as well. After every 100 message it should be committed, use non-durable/faster channel and it should be able to hold maximum 1000 events.
CCA175 Exam Question 35
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Problem Scenario 39 : You have been given two files
spark16/file1.txt
1,9,5
2,7,4
3,8,3
spark16/file2.txt
1 ,g,h
2 ,i,j
3 ,k,l
Load these two tiles as Spark RDD and join them to produce the below results
(l,((9,5),(g,h)))
(2, ((7,4), (i,j))) (3, ((8,3), (k,l)))
And write code snippet which will sum the second columns of above joined results (5+4+3).
Problem Scenario 39 : You have been given two files
spark16/file1.txt
1,9,5
2,7,4
3,8,3
spark16/file2.txt
1 ,g,h
2 ,i,j
3 ,k,l
Load these two tiles as Spark RDD and join them to produce the below results
(l,((9,5),(g,h)))
(2, ((7,4), (i,j))) (3, ((8,3), (k,l)))
And write code snippet which will sum the second columns of above joined results (5+4+3).