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NEW QUESTION: 1
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.
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:
NEW QUESTION: 2
AWS Elastic Beanstalk stores your application files and optionally server log files in_________.
A. Amazon Glacier
B. Amazon S3
C. Amazon EC2
D. Amazon Storage Gateway
AWS Elastic Beanstalk stores your application files and optionally server log files in Amazon S3. If you are using the AWS Management Console, Git, the AWS Toolkit for Visual Studio, or AWS Toolkit for Eclipse, an Amazon S3 bucket will be created in your account for you and the files you upload will be automatically copied from your local client to Amazon S3. Optionally, you may configure Elastic Beanstalk to copy your server log files every hour to Amazon S3. You do this by editing the environment configuration settings.