Your department creates regular analytics reports from your company’s log files. All log data is
collected in Amazon S3 and processed by daily Amazon Elastic MapReduce (EMR) jobs that
generate daily PDF reports and aggregated tables in .csv format for an Amazon Redshift data
warehouse.
Your CFO requests that you optimize the cost structure for this system.
Which of the following alternatives will lower costs without compromising average performance of
the system or data integrity for the raw data?

You are the new IT architect in a company that operates a mobile sleep tracking application.
When activated at night, the mobile app is sending collected data points of 1 kilobyte every 5
minutes to your backend.
The backend takes care of authenticating the user and writing the data points into an Amazon
DynamoDB table.
Every morning, you scan the table to extract and aggregate last night’s data on a per user basis,
and store the results in Amazon S3. Users are notified via Amazon SNS mobile push notifications
that new data is available, which is parsed and visualized by the mobile app. Currently you have
around 100k users who are mostly based out of North America.
You have been tasked to optimize the architecture of the backend system to lower cost.
What would you recommend? Choose 2 answers

You require the ability to analyze a large amount of data which is stored on Amazon S3 using
Amazon Elastic MapReduce.
You are using the cc2.8xlarge instance type, whose CPUs are mostly idle during processing.
Which of the below would be the most cost efficient way to reduce the runtime of the job?

A 3-Ber e-commerce web application is currently deployed on-premises, and will be migrated to
AWS for greater scalability and elasticity. The web tier currently shares read-only data using a
network distributed file system. The app server tier uses a clustering mechanism for discovery
and shared session state that depends on IP multicast. The database tier uses shared-storage
clustering to provide database failover capability, and uses several read slaves for scaling. Data
on all servers and the distributed file system directory is backed up weekly to off-site tapes.
Which AWS storage and database architecture meets the requirements of the application?

You need a persistent and durable storage to trace call activity of an IVR (Interactive Voice
Response) system. Call duration is mostly in the 2-3 minutes timeframe. Each traced call can be
either active or terminated. An external application needs to know each minute the list of currently
active calls. Usually there are a few calls/second, but once per month there is a periodic peak up
to 1000 calls/second for a few hours. The system is open 24/7 and any downtime should be

avoided. Historical data is periodically archived to files. Cost saving is a priority for this project.
What database implementation would better fit this scenario, keeping costs as low as possible?