弊社の資源はずっと改訂され、アップデートされていますから、緊密な相関関係があります。ClouderaのCCA175認証資格の認証試験を準備しているあなたは、自分がトレーニングを選んで、しかも次の問題を受かったほうがいいです。弊社の試験問題はほとんど毎月で一回アップデートしますから、あなたは市場で一番新鮮な、しかも依頼できる良い資源を得ることができることを保証いたします。 NewValidDumpsの試験トレーニング資料はClouderaのCCA175認証資格認定試験の１００パーセントの合格率を保証します。近年、IT領域で競争がますます激しくなります。 ClouderaのCCA175認証資格試験問題集はNewValidDumpsのIT領域の専門家が心を込めて研究したものですから、NewValidDumpsのClouderaのCCA175認証資格試験資料を手に入れると、あなたが美しい明日を迎えることと信じています。

Cloudera Certified CCA175 ここには、私たちは君の需要に応じます。

Cloudera Certified CCA175認証資格 - CCA Spark and Hadoop Developer Exam この認証を持っていたら、あなたは自分の夢を実現できます。 NewValidDumpsのClouderaのCCA175 資格関連題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。

この文は人々に知られています。試合と同じ、試験もそのどおりですよ。試験に準備する時間が十分ではないから、CCA175認証資格認定試験を諦めた人がたくさんいます。

Cloudera CCA175認証資格 - 我々の誠意を信じてください。

CCA175認証資格認定試験の準備を効率的にするために、どんなツールが利用に値するものかわかっていますか。私は教えてあげますよ。NewValidDumpsのCCA175認証資格問題集が一番頼もしい資料です。この問題集がIT業界のエリートに研究し出されたもので、素晴らしい練習資料です。この問題集は的中率が高くて、合格率が100％に達するのです。それはIT専門家達は出題のポイントをよく掴むことができて、実際試験に出題される可能性があるすべての問題を問題集に含めることができますから。不思議だと思っていますか。しかし、これは本当のことですよ。

自分のIT業界での発展を希望したら、ClouderaのCCA175認証資格試験に合格する必要があります。ClouderaのCCA175認証資格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認証資格試験に合格することができます。

CCA175 PDF DEMO:

QUESTION NO: 1
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: 2
. 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: 3
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: 4
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: 5
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

Salesforce Platform-App-Builder - もし受験したいなら、試験の準備をどのようにするつもりですか。 Huawei H40-111 - 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。 不合格になる場合或いはClouderaのOracle 1z1-071問題集がどんな問題があれば、私たちは全額返金することを保証いたします。 短時間でSAP C-TADM-23-JPN試験に一発合格したいなら、我々社のClouderaのSAP C-TADM-23-JPN資料を参考しましょう。 あなたはNewValidDumpsのClouderaのSalesforce Industries-CPQ-Developer問題集を購入した後、私たちは一年間で無料更新サービスを提供することができます。

Updated: May 28, 2022