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NEW QUESTION: 1
---

A. Option C
B. Option A
C. Option B
D. Option D
Answer: B

NEW QUESTION: 2
What happens when you attempt to compile and run the following code?
# include <vector>
# include <iostream>
# include <algorithm>
using namespace std;
template<class T>struct Out {
ostream & out;
Out(ostream & o): out(o){}
void operator() (const T & val ) { out<<val<<" "; } };
int main() {
int t1[]={3,2,4,1,5};
int t2[]={6,10,8,7,9};
vector<int> v1(10);
sort(t1, t1+5); sort(t2, t2+5);
copy(t1,t1+5,v1.begin());
copy(t2,t2+5,v1.begin()+5);
merge(v1.begin(), v1.begin()+5,v1.end());
for_each(v1.begin(), v1.end(), Out<int>(cout));cout<<endl;
return 0;
}
Program outputs:
A. 3 2 4 1 5 6 7 8 9 10
B. 1 2 3 4 5 6 7 8 9 10
C. 1 2 3 4 5 6 10 8 7 9
D. compilation error
E. 3 2 4 1 5 6 10 8 7 9
Answer: D

NEW QUESTION: 3
Solutions Architect는 인터넷에 연결된 2,000 개의 센서에서 데이터를 수집하고 저장할 시스템을 설계하고 있습니다. 각 센서는 1 초마다 1KB의 데이터를 생성합니다. 데이터는 시스템으로 전송되고 분석을 위해 무기한 저장된 후 몇 초 내에 분석 할 수 있어야 합니다. 데이터를 수집하고 저장하는 가장 비용 효율적인 솔루션은 무엇입니까?
A. 각 레코드를 Amazon DynamoDB 테이블에 넣습니다. 테이블을 쿼리하여 최근 데이터를 분석하십시오.
DynamoDB 스트림에 연결된 AWS Lambda 함수를 사용하여 레코드를 그룹화하고 Amazon S3의 객체에 기록한 다음 DynamoDB 테이블에서 레코드를 삭제하십시오. DynamoDB 테이블의 최근 데이터와 Amazon S3의 히스토리 데이터 분석
B. 레코드를 시간 단위로 구성하고 레코드 키를 해시하는 접두사가 붙은 Amazon S3의 객체에 각 레코드를 넣습니다. S3 수명주기 관리를 사용하여 객체를 S3 드문 액세스 스토리지로 전환하여 스토리지 비용을 줄입니다. Amazon S3의 데이터에 액세스하여 최근 및 과거 데이터 분석
C. 각 레코드를 Amazon Kinesis Data Streams에 넣습니다. AWS Lambda 함수를 사용하여 시간별로 레코드를 구성하고 레코드 키를 해시하는 접두사를 사용하여 Amazon S3의 객체에 각 레코드를 씁니다. Kinesis Data Streams의 최근 데이터와 Amazon S3의 히스토리 데이터를 분석하십시오.
D. 각 레코드를 Amazon Kinesis Data Streams에 넣습니다. 스트림에서 레코드를 읽고 Amazon S3에서 객체로 그룹화하도록 Amazon Kinesis Data Firehouse를 설정합니다. Kinesis Data Streams의 최근 데이터와 Amazon S3의 히스토리 데이터를 분석하십시오.
Answer: A