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Cloudera Certified CCA175出題内容 - CCA Spark and Hadoop Developer Exam 常々、時間とお金ばかり効果がないです。 PDF、オンライン、ソフトの3つのバーションのClouderaのCCA175 日本語試験対策試験の資料は独自の長所があってあなたは我々のデモを利用してから自分の愛用する版を選ぶことができます。学生時代に出てから、私たちはもっと多くの責任を持って勉強する時間は少なくなりました。

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Cloudera CCA175出題内容 - 暇の時間を利用して勉強します。

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多分、CCA175出題内容テスト質問の数が伝統的な問題の数倍である。Cloudera　CCA175出題内容試験参考書は全ての知識を含めて、全面的です。

CCA175 PDF DEMO:

QUESTION NO: 1
CORRECT TEXT
Problem Scenario 13 : You have been given following mysql database details as well as other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Create a table in retailedb with following definition.
CREATE table departments_export (department_id int(11), department_name varchar(45), created_date T1MESTAMP DEFAULT NOWQ);
2. Now import the data from following directory into departments_export table,
/user/cloudera/departments new
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Login to musql db
mysql --user=retail_dba -password=cloudera
show databases; use retail_db; show tables;
step 2 : Create a table as given in problem statement.
CREATE table departments_export (departmentjd int(11), department_name varchar(45), created_date T1MESTAMP DEFAULT NOW()); show tables;
Step 3 : Export data from /user/cloudera/departmentsnew to new table departments_export sqoop export -connect jdbc:mysql://quickstart:3306/retail_db \
-username retaildba \
--password cloudera \
--table departments_export \
-export-dir /user/cloudera/departments_new \
-batch
Step 4 : Now check the export is correctly done or not. mysql -user*retail_dba - password=cloudera show databases; use retail _db;
show tables;
select' from departments_export;

QUESTION NO: 2
Select top 2 products by price
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Select all the products which has product code as null
val results = sqlContext.sql(......SELECT' FROM products WHERE code IS NULL......) results. showQ val results = sqlContext.sql(......SELECT * FROM products WHERE code = NULL ",,M ) results.showQ
Step 2 : Select all the products , whose name starts with Pen and results should be order by Price descending order. val results = sqlContext.sql(......SELECT * FROM products
WHERE name LIKE 'Pen %' ORDER BY price DESC......)
results. showQ
Step 3 : Select all the products , whose name starts with Pen and results should be order by Price descending order and quantity ascending order. val results = sqlContext.sql('.....SELECT * FROM products WHERE name LIKE 'Pen %' ORDER BY price DESC, quantity......) results. showQ
Step 4 : Select top 2 products by price
val results = sqlContext.sql(......SELECT' FROM products ORDER BY price desc
LIMIT2......}
results. show()
4. CORRECT TEXT
Problem Scenario 4: 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.
Import Single table categories (Subset data} to hive managed table , where category_id between 1 and 22
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table (Subset data)
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=categories -where "\'category_id\' between 1 and 22" --hive- import --m 1
Note: Here the ' is the same you find on ~ key
This command will create a managed table and content will be created in the following directory.
/user/hive/warehouse/categories
Step 2 : Check whether table is created or not (In Hive)
show tables;
select * from categories;

QUESTION NO: 3
CORRECT TEXT
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:
Solution
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-jar> \
[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.

QUESTION NO: 4
. Create a Hive parquet table using SparkSQL and load data in it.
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create this tile in HDFS under following directory (Without header}
/user/cloudera/he/exam/task1/productcsv
Step 2 : Now using Spark-shell read the file as RDD
// load the data into a new RDD
val products = sc.textFile("/user/cloudera/he/exam/task1/product.csv")
// Return the first element in this RDD
prod u cts.fi rst()
Step 3 : Now define the schema using a case class
case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price:
Float)
Step 4 : create an RDD of Product objects
val prdRDD = products.map(_.split(",")).map(p =>
Product(p(0).tolnt,p(1),p(2),p(3}.tolnt,p(4}.toFloat))
prdRDD.first()
prdRDD.count()
Step 5 : Now create data frame val prdDF = prdRDD.toDF()
Step 6 : Now store data in hive warehouse directory. (However, table will not be created } import org.apache.spark.sql.SaveMode
prdDF.write.mode(SaveMode.Overwrite).format("orc").saveAsTable("product_orc_table") step 7:
Now create table using data stored in warehouse directory. With the help of hive.
hive
show tables
CREATE EXTERNAL TABLE products (productid int,code string,name string .quantity int, price float}
STORED AS ore
LOCATION 7user/hive/warehouse/product_orc_table';
Step 8 : Now create a parquet table
import org.apache.spark.sql.SaveMode
prdDF.write.mode(SaveMode.Overwrite).format("parquet").saveAsTable("product_parquet_ table")
Step 9 : Now create table using this
CREATE EXTERNAL TABLE products_parquet (productid int,code string,name string
.quantity int, price float}
STORED AS parquet
LOCATION 7user/hive/warehouse/product_parquet_table';
Step 10 : Check data has been loaded or not.
Select * from products;
Select * from products_parquet;
3. CORRECT TEXT
Problem Scenario 84 : In Continuation of previous question, please accomplish following activities.
1. Select all the products which has product code as null
2. Select all the products, whose name starts with Pen and results should be order by Price descending order.
3. Select all the products, whose name starts with Pen and results should be order by
Price descending order and quantity ascending order.

QUESTION NO: 5
CORRECT TEXT
Problem Scenario 35 : You have been given a file named spark7/EmployeeName.csv
(id,name).
EmployeeName.csv
E01,Lokesh
E02,Bhupesh
E03,Amit
E04,Ratan
E05,Dinesh
E06,Pavan
E07,Tejas
E08,Sheela
E09,Kumar
E10,Venkat
1. Load this file from hdfs and sort it by name and save it back as (id,name) in results directory.
However, make sure while saving it should be able to write In a single file.
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution:
Step 1 : Create file in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark7/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 3 : Now swap namePairRDD RDD.
val swapped = namePairRDD.map(item => item.swap)
step 4: Now sort the rdd by key.
val sortedOutput = swapped.sortByKey()
Step 5 : Now swap the result back
val swappedBack = sortedOutput.map(item => item.swap}
Step 6 : Save the output as a Text file and output must be written in a single file.
swappedBack. repartition(1).saveAsTextFile("spark7/result.txt")

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Updated: May 28, 2022