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NEW QUESTION: 1
EIRGPスタブコマンドでルーターが構成されました。ルーターはどのタイプのルートをアドバタイズしますか？
A. 接続され静的
B. 接続済み、静的、および概要
C. 静的および要約
D. 接続および要約
Answer: D
NEW QUESTION: 2
While preparing for a kidney biopsy the nurse should position the patient:
A. Lateral opposite to biopsy site
B. Lateral flexed knee-chest
C. Supine in bed with knee flexion
D. Prone with a sandbag under the abdomen
Answer: D
NEW QUESTION: 3
攻撃者が攻撃側のマシンのMACアドレスを送信して、有効なサーバーのIPアドレスにMACを解決することで応答する場合、次のタイプの攻撃のどれが使用されていますか？
A. ARP中毒
ARPスプーフィングは、ARPポイズニングとも呼ばれ、攻撃者がネットワークデバイス間の通信を傍受できるようにする中間者（MitM）攻撃です。攻撃は次のように機能します。攻撃者はネットワークにアクセスできる必要があります。
B. 邪悪な双子
C. セッションハイジャック
D. IPスプーフィング
Answer: A
NEW QUESTION: 4
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.
3 . Create a Hive parquet table using SparkSQL and load data in it.
Answer:
Explanation:
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;