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NEW QUESTION: 1
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Azure Security Centerを使用して、3つのAzureサブスクリプションの一元化されたポリシー管理を行います。
いくつかのポリシー定義を使用して、サブスクリプションのセキュリティを管理します。
3つのサブスクリプションすべてにポリシー定義をグループとして展開する必要があります。
解決策：リソースグラフと、管理グループを対象とする割り当てを作成します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
References:
https://4sysops.com/archives/apply-governance-policy-to-multiple-azure-subscriptions-with-management-groups/
NEW QUESTION: 2
What are three goals of subscriber?
A. Writebale databse
B. Place the data to CDR
C. Provides call processing
D. Provides call forwarding
E. Read only database
F. Provides Admin Rights
G. Place the data to COR
Answer: B,C,E
NEW QUESTION: 3
Consider the following program code:
$x = 150;
$y = "250";
if (($x + 100) == $y) { print("1 "); }
if ("250" == $y) { print("2 "); }
if ("250" eq $y) { print("3 "); }
if ($x lt $y) { print("4 "); }
if ($x ge $y) { print("5 "); }
What is the result of executing this program code?
A. The code will output the following:
1 3 5
B. The code will output the following:
1 2 3 4
C. The code will output the following:
1 2 3 4 5
D. The code will output the following:
1 3 4
Answer: B
NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 45 : You have been given 2 files , with the content as given Below
(spark12/technology.txt)
(spark12/salary.txt)
(spark12/technology.txt)
first,last,technology
Amit,Jain,java
Lokesh,kumar,unix
Mithun,kale,spark
Rajni,vekat,hadoop
Rahul,Yadav,scala
(spark12/salary.txt)
first,last,salary
Amit,Jain,100000
Lokesh,kumar,95000
Mithun,kale,150000
Rajni,vekat,154000
Rahul,Yadav,120000
Write a Spark program, which will join the data based on first and last name and save the joined results in following format, first Last.technology.salary
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create 2 files first using Hue in hdfs.
Step 2 : Load all file as an RDD
val technology = sc.textFile(Msparkl2/technology.txt").map(e => e.splitf',")) val salary = sc.textFile("spark12/salary.txt").map(e => e.split("."))
Step 3 : Now create Key.value pair of data and join them.
val joined = technology.map(e=>((e(0),e(1)),e(2))).join(salary.map(e=>((e(0),e(1)),e(2))))
Step 4 : Save the results in a text file as below.
joined.repartition(1).saveAsTextFile("spark12/multiColumn Joined.txt")