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NEW QUESTION: 1
Given the following code sample: FFilename++IPEASF.....L.....
A. DName+++++++++++ETDsFrom+++To/L+++IDc.Keywords++++++++++++ d MyCustomer e ds extname(customer)
B. DName+++++++++++ETDsFrom+++To/L+++IDc.Keywords++++++++++++
d MyCustomer ds likerec(custrec)
C. Device+.Keywords++++++++++++++++++++
fcustomer if e k disk rename(customer:custrec)
Which of the following data specifications produces a qualified data structure with subfields
matching the layout of file CUSTOMER?
D. DName+++++++++++ETDsFrom+++To/L+++IDc.Keywords++++++++++++
d MyCustomer e ds likerec(customer)
E. DName+++++++++++ETDsFrom+++To/L+++IDc.Keywords++++++++++++
d MyCustomer ds extname(custrec)
Answer: E

NEW QUESTION: 2
You are designing a service that aggregates clickstream data in batch and delivers reports to subscribers via
email only once per week. Data is extremely spikey, geographically distributed, high-scale, and unpredictable.
How should you design this system?
A. Use a large RedShift cluster to perform the analysis, and a fleet of Lambdas to perform record inserts
into the RedShift tables. Lambda will scale rapidly enough for the traffic spikes.
B. Use a CloudFront distribution with access log delivery to S3. Clicks should be recorded as querystring
GETs to the distribution. Reports are built and sent by periodically running EMRjobs over the access
logs in S3.
C Use API Gateway invoking Lambdas which PutRecords into Kinesis, and EMR running Spark
performing GetRecords on Kinesis to scale with spikes. Spark on EMR outputs the analysis to S3, which
are sent out via email.
D- Use AWS Elasticsearch service and EC2 Auto Scaling groups. The Autoscaling groups scale based
on click throughput and stream into the Elasticsearch domain, which is also scalable. Use Kibana to
generate reports periodically.
Answer: B
Explanation:
Explanation
When you look at building reports or analyzing data from a large data set, you need to consider CMR because
this service is built on the Hadoop framework which is used to processes large data sets.
The ideal approach to getting data onto CMR is to use S3. Since the Data is extremely spikey and
geographically distributed, using edge locations via Cloudfront distributions is the best way to fetch the data.
Option A is invalid because RedShift is more of a petabyte storage cluster.
Option C is invalid because having both Kinesis and CMR for the job analysis is redundant.
Option D is invalid because Elastic Search is not an option for processing records.
For more information on Amazon CMR, please visit the below URL:
* https://aws.amazon.com/emr/

NEW QUESTION: 3
Solutions Architect가 10TB PostgreSQL 데이터베이스를 PostgreSQL 용 Amazon RDS로 마이그레이션하고 있습니다. 회사의 인터넷 링크는 Amazon VPC에서 VPN을 사용하여 50MB이며 솔루션 아키텍트는 컷 오버 전에 데이터를 마이그레이션하고 변경 사항을 동기화해야 합니다.
컷 오버는 8 일 이내에 이루어져야 합니다.
데이터베이스를 안전하고 안정적으로 마이그레이션하는 가장 복잡한 방법은 무엇입니까?
A. 온 프레미스에서 AWS로 데이터를 지속적으로 복제하는 AWS DMS 작업을 생성하십시오. 데이터가 동기화 된 후 Amazon RDS로 컷 오버
B. AWS Schema Conversion Tool을 사용하여 AWS Snowball 디바이스를 주문하고 데이터베이스를 복사하십시오.
Amazon S3에서 데이터를 사용할 수 있으면 AWS DMS를 사용하여 Amazon RDS에 데이터를 로드하고 컷 오버 전에 변경 사항을 동기화하도록 작업을 구성하십시오.
C. AWS Snowball 디바이스를 주문하고 디바이스에 데이터베이스 덤프를 복사하십시오. 데이터가 Amazon S3에 복사 된 후 Amazon RDS 인스턴스로 가져옵니다. 컷 오버 전에 변경 사항을 동기화하도록 VPN을 통한 로그 전달을 설정하십시오.
D. AWS Snowball 디바이스를 주문하고 AWS DMS를 사용하여 데이터베이스를 복사하십시오. Amazon 3에서 데이터베이스를 사용할 수있는 경우 AWS DMS를 사용하여 데이터베이스를 Amazon RDS에 로드하고 컷 오버 전에 변경 사항을 동기화하도록 작업을 구성하십시오.
Answer: A