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NEW QUESTION: 1
한 대기업이 전체 IT 포트폴리오를 AWS로 마이그레이션 하고 있습니다. 회사의 각 사업 단위에는 개발 및 테스트 환경을 모두 지원하는 독립 실행 형 AWS 계정이 있습니다. 프로덕션 워크로드를 지원하기 위한 새 계정이 곧 필요합니다.
재무 부서는 중앙 집중식 지불 방법이 필요하지만 비용을 할당하기 위해 각 그룹의 지출에 대한 가시성을 유지해야 합니다.
보안 팀은 모든 회사 계정에서 IAM 사용을 제어하기 위한 중앙 집중식 메커니즘이 필요합니다.
최소한의 노력으로 회사의 요구를 충족하는 다음 옵션의 조합은 무엇입니까? (2 개 선택)
A. 각 사업부가 자체 AWS 계정을 사용하도록 요구합니다. 각 AWS 계정에 적절하게 태그를 지정하고 비용 탐색기를 활성화 하여 지불 거절을 관리합니다.
B. AWS Organizations를 사용하여 선택한 지불 자 계정에서 새 조직을 만들고 조직 단위 계층을 정의합니다. 조직에 가입하도록 기존 계정을 초대하고 조직을 사용하여 새 계정을 만듭니다.
C. 각 계정에서 시작되는 공통 IAM 권한을 정의하는 매개 변수화 된 AWS CloudFormation 템플릿 모음을 사용합니다. 최소 권한 모델을 적용하려면 모든 신규 및 기존 계정이 적절한 스택을 시작해야 합니다.
D. 회사의 모든 AWS 계정을 단일 AWS 계정으로 통합합니다. 청구 목적으로 태그를 사용하고 최소 권한 모델을 적용하려면 IAM의 액세스 조언 기능을 사용하십시오.
E. AWS Organizations의 모든 기능을 활성화하고 하위 계정에 대한 IAM 권한을 필터링하는 적절한 서비스 제어 정책을 설정합니다.
Answer: A,E
Explanation:
Explanation
https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/ce-what-is.html
NEW QUESTION: 2
You need to enable telemetry message tracing through the entire IoT solution.
What should you do?
A. Monitor device lifecycle events.
B. Enable the DeviceTelemetry diagnostic log and stream the log data to an Azure event hub.
C. Upload IoT device logs by using the File upload feature.
D. Implement distributed tracing.
Answer: D
Explanation:
IoT Hub is one of the first Azure services to support distributed tracing. As more Azure services support distributed tracing, you'll be able trace IoT messages throughout the Azure services involved in your solution.
Note:
Enabling distributed tracing for IoT Hub gives you the ability to:
Precisely monitor the flow of each message through IoT Hub using trace context. This trace context includes correlation IDs that allow you to correlate events from one component with events from another component. It can be applied for a subset or all IoT device messages using device twin.
Automatically log the trace context to Azure Monitor diagnostic logs.
Measure and understand message flow and latency from devices to IoT Hub and routing endpoints. Start considering how you want to implement distributed tracing for the non-Azure services in your IoT solution.
Reference:
https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-distributed-tracing
NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 37 : ABCTECH.com has done survey on their Exam Products feedback using a web based form. With the following free text field as input in web ui.
Name: String
Subscription Date: String
Rating : String
And servey data has been saved in a file called spark9/feedback.txt
Christopher|Jan 11, 2015|5
Kapil|11 Jan, 2015|5
Thomas|6/17/2014|5
John|22-08-2013|5
Mithun|2013|5
Jitendra||5
Write a spark program using regular expression which will filter all the valid dates and save in two separate file (good record and bad record)
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create a file first using Hue in hdfs.
Step 2 : Write all valid regular expressions sysntex for checking whether records are having valid dates or not.
val regl =......(\d+)\s(\w{3})(,)\s(\d{4}).......r//11 Jan, 2015
val reg2 =......(\d+)(U)(\d+)(U)(\d{4})......s II 6/17/2014
val reg3 =......(\d+)(-)(\d+)(-)(\d{4})""".r//22-08-2013
val reg4 =......(\w{3})\s(\d+)(,)\s(\d{4})......s II Jan 11, 2015
Step 3 : Load the file as an RDD.
val feedbackRDD = sc.textFile("spark9/feedback.txt"}
Step 4 : As data are pipe separated , hence split the same. val feedbackSplit = feedbackRDD.map(line => line.split('|'))
Step 5 : Now get the valid records as well as , bad records.
val validRecords = feedbackSplit.filter(x =>
(reg1.pattern.matcher(x(1).trim).matches|reg2.pattern.matcher(x(1).trim).matches|reg3.patt ern.matcher(x(1).trim).matches | reg4.pattern.matcher(x(1).trim).matches)) val badRecords = feedbackSplit.filter(x =>
!(reg1.pattern.matcher(x(1).trim).matches|reg2.pattern.matcher(x(1).trim).matches|reg3.pat tern.matcher(x(1).trim).matches | reg4.pattern.matcher(x(1).trim).matches))
Step 6 : Now convert each Array to Strings
val valid =vatidRecords.map(e => (e(0),e(1),e(2)))
val bad =badRecords.map(e => (e(0),e(1),e(2)))
Step 7 : Save the output as a Text file and output must be written in a single tile, valid.repartition(1).saveAsTextFile("spark9/good.txt") bad.repartition(1).saveAsTextFile("sparkS7bad.txt")