NewValidDumpsはIT認定試験を受験した多くの人々を助けました。また、受験生からいろいろな良い評価を得ています。NewValidDumpsのCCA175対応問題集問題集の合格率が100%に達することも数え切れない受験生に証明された事実です。 ほかのホームページに弊社みたいな問題集を見れば、あとでみ続けて、弊社の商品を盗作することとよくわかります。NewValidDumpsが提供した資料は最も全面的で、しかも更新の最も速いです。 それはNewValidDumpsのCCA175対応問題集問題集です。

Cloudera Certified CCA175 無料な部分ダウンロードしてください。

Cloudera Certified CCA175対応問題集 - CCA Spark and Hadoop Developer Exam ここには、私たちは君の需要に応じます。 NewValidDumps のClouderaのCCA175 資格講座認証証明書はあなたが自分の知識と技能を高めることに助けになれることだけでなく、さまざまな条件であなたのキャリアを助けることもできます。NewValidDumps のClouderaのCCA175 資格講座問題集を利用することをお勧めいたします。

NewValidDumpsのClouderaのCCA175対応問題集試験トレーニング資料は試験問題と解答を含まれて、豊富な経験を持っているIT業種の専門家が長年の研究を通じて作成したものです。その権威性は言うまでもありません。うちのClouderaのCCA175対応問題集試験トレーニング資料を購入する前に、NewValidDumpsのサイトで、一部分のフリーな試験問題と解答をダンロードでき、試用してみます。

Cloudera CCA175対応問題集 - 心配することはないです。

自分のIT業界での発展を希望したら、ClouderaのCCA175対応問題集試験に合格する必要があります。ClouderaのCCA175対応問題集試験はいくつ難しくても文句を言わないで、我々NewValidDumpsの提供する資料を通して、あなたはClouderaのCCA175対応問題集試験に合格することができます。ClouderaのCCA175対応問題集試験を準備しているあなたに試験に合格させるために、我々NewValidDumpsは模擬試験ソフトを更新し続けています。

NewValidDumpsをクロックしたら、１００パーセントの成功を差し上げます。IT職員としてのあなたは昇進したいのですか。

CCA175 PDF DEMO:

QUESTION NO: 1
CORRECT TEXT
Problem Scenario 49 : You have been given below code snippet (do a sum of values by key}, with intermediate output.
val keysWithValuesList = Array("foo=A", "foo=A", "foo=A", "foo=A", "foo=B", "bar=C",
"bar=D", "bar=D")
val data = sc.parallelize(keysWithValuesl_ist}
//Create key value pairs
val kv = data.map(_.split("=")).map(v => (v(0), v(l))).cache()
val initialCount = 0;
val countByKey = kv.aggregateByKey(initialCount)(addToCounts, sumPartitionCounts)
Now define two functions (addToCounts, sumPartitionCounts) such, which will produce following results.
Output 1
countByKey.collect
res3: Array[(String, Int)] = Array((foo,5), (bar,3))
import scala.collection._
val initialSet = scala.collection.mutable.HashSet.empty[String]
val uniqueByKey = kv.aggregateByKey(initialSet)(addToSet, mergePartitionSets)
Now define two functions (addToSet, mergePartitionSets) such, which will produce following results.
Output 2:
uniqueByKey.collect
res4: Array[(String, scala.collection.mutable.HashSet[String])] = Array((foo,Set(B, A}},
(bar,Set(C, D}}}
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
val addToCounts = (n: Int, v: String) => n + 1
val sumPartitionCounts = (p1: Int, p2: Int} => p1 + p2
val addToSet = (s: mutable.HashSet[String], v: String) => s += v
val mergePartitionSets = (p1: mutable.HashSet[String], p2: mutable.HashSet[String]) => p1
+ += p2

QUESTION NO: 2
CORRECT TEXT
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.

QUESTION NO: 3
. 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: 4
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: 5
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;

IIBA ECBA - 世の中に去年の自分より今年の自分が優れていないのは立派な恥です。 Fortinet NSE5_FAZ-7.2-JPN - IT職員のキャリアと関連しますから。 また、BICSI DCDC-003.1問題集に疑問があると、メールで問い合わせてください。 CIPS L4M1 - どうですか。 だから、我々社は力の限りで弊社のCloudera　Microsoft SC-400試験資料を改善し、改革の変更に応じて更新します。

Updated: May 28, 2022