Answer: C

Explanation:

Billing commences when Amazon EC2 initiates the boot sequence of an AM instance. Billing ends when the instance shuts down, which could occur through a web services command, by running "shutdown -h", or through instance failure.

Reference: http://aws.amazon.com/ec2/faqs/#BiIIing

Answer: C

Answer: B 

Explanation:

In AWS CIoudHSM, you can perform a remote backup/restore of a Luna SA partition if you have purchased a Luna Backup HSM.

Reference:  http://docs.aws.amazon.com/cloudhsm/latest/userguide/cloud-hsm-backup-restore.html

Answer: A

Answer: D

Explanation:

A Virtual Private Cloud (VPC) is a virtual network dedicated to the user’s AWS account. A user can create a subnet with VPC and launch instances inside that subnet. When the user is launching an instance he  needs to select an option which attaches a public IP to the instance. If the user has not selected the option  to attach the public IP then it will only have a private IP when launched. If the user wants to connect to

an instance from the internet he should create an elastic IP with VPC. If the elastic IP is a part of EC2

Classic it cannot be assigned to a VPC instance.

Reference:  http://docs.aws.amazon.com/AmazonVPC/Iatest/GettingStartedGuide/LaunchInstance.htmI

Answer: D

Answer: C

Explanation:

Amazon EC2 provides a virtual computing environments, known as an instance.

After you launch an instance, AWS recommends that you check its status to confirm that it goes from the pending status to the running status, the not terminated status.

The following are a few reasons why an Amazon EBS-backed instance might immediately terminate: You've reached your volume limit.

The AM is missing a required part. The snapshot is corrupt. Reference:

http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Using_|nstanceStraightToTerminated.html

Answer: A 

Explanation:

EIastiCache for Memcached

The primary goal of caching is typically to offload reads from your database or other primary data source. In most apps, you have hot spots of data that are regularly queried, but only updated periodically. Think of the front page of a blog or news site, or the top 100 leaderboard in an online game. In this type of case, your app can receive dozens, hundreds, or even thousands of requests for the same data before it's   updated again. Having your caching layer handle these queries has several advantages. First, it's considerably cheaper to add an in-memory cache than to scale up to a larger database cluster. Second,

an in-memory cache is also easier to scale out, because it's easier to distribute an in-memory cache horizontally than a relational database.

Last, a caching layer provides a request buffer in the event of a sudden spike in usage. If your app or game ends up on the front page of Reddit or the App Store, it's not unheard of to see a spike that is 10 to 100 times your normal application load. Even if you autoscale your application instances, a IOx request spike will likely make your database very unhappy.

Let's focus on EIastiCache for Memcached first, because it is the best fit for a caching focused solution. We'II revisit Redislater in the paper, and weigh its advantages and disadvantages.

Architecture with EIastiCache for Memcached

When you deploy an EIastiCache Memcached cluster, it sits in your application as a separate tier alongside your database. As mentioned previously, Amazon EIastiCache does not directly communicate with your database tier, or indeed have any particular knowledge of your database. A simplified deployment for a web application looks something like this:

In this architecture diagram, the Amazon EC2 application instances are in an Auto Scaling group, located behind a load balancer using Elastic Load Balancing, which distributes requests among the instances. As requests come into a given EC2 instance, that EC2 instance is responsible for communicating with EIastiCache and the database tier. For development purposes, you can begin with a single EIastiCache node to test your application, and then scale to additional cluster nodes by modifying t he EIastiCache cluster. As you add additional cache nodes, the EC2 application instances are able to distribute cache  keys across multiple EIastiCache nodes. The most common practice is to use client-side sharding to distribute keys across cache nodes, which we will discuss later in this paper.

When you launch an EIastiCache cluster, you can choose the Availability Zone(s) that the cluster lives in. For best performance, you should configure your cluster to use the same Availability Zones as your application servers. To launch an EIastiCache cluster in a specific Availability Zone, make sure to specify the Preferred Zone(s) option during cache cluster creation. The Availability Zones that you specify will be where EIastiCache will launch your cache nodes. We recommend that you select Spread Nodes Across Zones, which tells EIastiCache to distribute cache nodes across these zones as evenly as possible. This distribution will mitigate the impact of an Availability Zone disruption on your E|astiCache nodes. The trade-off is that some of the requests from your application to EIastiCache will go to a node in a different Availability Zone, meaning latency will be slightly higher.

For more details, refer to Creating a Cache Cluster in the Amazon EIastiCache User Guide.

As mentioned at the outset, EIastiCache can be coupled with a wide variety of databases. Here is an example architecture that uses Amazon DynamoDB instead of Amazon RDS and IV|ySQL:

This combination of DynamoDB and EIastiCache is very popular with mobile and game companies, because DynamoDB allows for higher write throughput at lower cost than traditional relational databases. In addition, DynamoDB uses a key-value access pattern similar to EIastiCache, which also simplifies the programming model. Instead of using relational SQL for the primary database but then key-value patterns for the cache, both the primary database and cache can be programmed similarly.

In this architecture pattern, DynamoDB remains the source of truth for data, but application reads are offloaded to EIastiCache for a speed boost.

Answer: C

Explanation:

In EBS workload demand plays an important role in getting the most out of your General Purpose (SSD) and Provisioned IOPS (SSD) volumes. In order for your volumes to deliver the amount of IOPS that are available, they need to have enough I/O requests sent to them. There is a relationship between the demand on the volumes, the amount of IOPS that are available to them, and the latency of the request (the amount of time it takes for the I/O operation to complete).

Latency is the true end-to-end client time of an I/O operation; in other words, when the client sends a IO, how long does it take to get an acknowledgement from the storage subsystem that the IO read or write is complete.

If your I/O latency is higher than you require, check your average queue length to make sure that your application is not trying to drive more IOPS than you have provisioned. You can maintain high IOPS while keeping latency down by maintaining a low average queue length (which is achieved by provisioning   more IOPS for your volume).

Reference:  http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-workload-demand.htmI

Answer: B

Explanation:

Only Internal DNS name can be resolved within Amazon EC2. Reference:

http://docs.amazonwebservices.com/AWSEC2/latest/UserGuide/using-instance-addressing.htmI