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You need your CI to build AMIs with code pre-installed on the images on every new code push. You need to do this as cheaply as possible. How do you do this?
A. Bid on spot instances just above the asking price as soon as new commits come in, perform all instance configuration and setup, then create an AMI based on the spot instance.
B. Have the CI launch a new on-demand EC2 instance when new commits come in, perform all instance configuration and setup, then create an AMI based on the on-demand instance.
C. Purchase a Light Utilization Reserved Instance to save money on the continuous integration machine.
Use these credits whenever your create AMIs on instances.
D. When the CI instance receives commits, attach a new EBS volume to the CI machine. Perform all setup on this EBS volume so you don’t need a new EC2 instance to create the AMI.
Spot instances are the cheapest option, and you can use minimum run duration if your AMI takes more than a few minutes to create.
Spot instances are also available to run for a predefined duration – in hourly increments up to six hours in length – at a significant discount (30-45%) compared to On-Demand pricing plus an additional 5% during off-peak times1 for a total of up to 50% savings.
When thinking of DynamoDB, what are true of Global Secondary Key properties?
A. The partition key and sort key can be different from the table.
B. Only the partition key can be different from the table.
C. Either the partition key or the sort key can be different from the table, but not both.
D. Only the sort key can be different from the table.
Global secondary index — an index with a partition key and a sort key that can be different from those on the table. A global secondary index is considered “global” because queries on the index can span all of the data in a table, across all partitions.
You need to process long-running jobs once and only once. How might you do this?
A. Use an SNS queue and set the visibility timeout to long enough for jobs to process.
B. Use an SQS queue and set the reprocessing timeout to long enough for jobs to process.
C. Use an SQS queue and set the visibility timeout to long enough for jobs to process.
D. Use an SNS queue and set the reprocessing timeout to long enough for jobs to process.
The message timeout defines how long after a successful receive request SQS waits before allowing jobs to be seen by other components, and proper configuration prevents duplicate processing.
You are getting a lot of empty receive requests when using Amazon SQS.
This is making a lot of unnecessary network load on your instances.
What can you do to reduce this load?
A. Subscribe your queue to an SNS topic instead.
B. Use as long of a poll as possible, instead of short polls.
C. Alter your visibility timeout to be shorter.
D. Use <code>sqsd</code> on your EC2 instances.
One benefit of long polling with Amazon SQS is the reduction of the number of empty responses, when there are no messages available to return, in reply to a ReceiveMessage request sent to an Amazon SQS queue. Long polling allows the Amazon SQS service to wait until a message is available in the queue before sending a response.
You need to know when you spend $1000 or more on AWS. What’s the easy way for you to see that notification?
A. AWS CloudWatch Events tied to API calls, when certain thresholds are exceeded, publish to SNS.
B. Scrape the billing page periodically and pump into Kinesis.
C. AWS CloudWatch Metrics + Billing Alarm + Lambda event subscription. When a threshold is exceeded, email the manager.
D. Scrape the billing page periodically and publish to SNS.
Even if you’re careful to stay within the free tier, it’s a good idea to create a billing alarm to notify you if you exceed the limits of the free tier. Billing alarms can help to protect you against unknowingly accruing charges if you inadvertently use a service outside of the free tier or if traffic exceeds your expectations. http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/free-tier-alarms.html
You need to grant a vendor access to your AWS account. They need to be able to read protected messages in a private S3 bucket at their leisure. They also use AWS. What is the best way to accomplish this?
A. Create an IAM User with API Access Keys. Grant the User permissions to access the bucket. Give the vendor the AWS Access Key ID and AWS Secret Access Key for the User.
B. Create an EC2 Instance Profile on your account. Grant the associated IAM role full access to the bucket. Start an EC2 instance with this Profile and give SSH access to the instance to the vendor.
C. Create a cross-account IAM Role with permission to access the bucket, and grant permission to use the Role to the vendor AWS account.
D. Generate a signed S3 PUT URL and a signed S3 PUT URL, both with wildcard values and 2 year durations. Pass the URLs to the vendor.
When third parties require access to your organization’s AWS resources, you can use roles to delegate access to them. For example, a third party might provide a service for managing your AWS resources. With IAM roles, you can grant these third parties access to your AWS resources without sharing your AWS security credentials. Instead, the third party can access your AWS resources by assuming a role that you create in your AWS account.
Your serverless architecture using AWS API Gateway, AWS Lambda, and AWS DynamoDB experienced a large increase in traffic to a sustained 400 requests per second, and dramatically increased in failure rates. Your requests, during normal operation, last 500 milliseconds on average. Your DynamoDB table did not exceed 50% of provisioned throughput, and Table primary keys are designed correctly. What is the most likely issue?
A. Your API Gateway deployment is throttling your requests.
B. Your AWS API Gateway Deployment is bottlenecking on request (de)serialization.
C. You did not request a limit increase on concurrent Lambda function executions.
D. You used Consistent Read requests on DynamoDB and are experiencing semaphore lock.
AWS API Gateway by default throttles at 500 requests per second steady-state, and 1000 requests per second at spike. Lambda, by default, throttles at 100 concurrent requests for safety. At 500 milliseconds (half of a second) per request, you can expect to support 200 requests per second at 100 concurrency. This is less than the 400 requests per second your system now requires. Make a limit increase request via the AWS Support Console.
AWS Lambda: Concurrent requests safety throttle per account -> 100 http://docs.aws.amazon.com/general/latest/gr/aws_service_limits.html#limits_lambda
Why are more frequent snapshots or EBS Volumes faster?
A. Blocks in EBS Volumes are allocated lazily, since while logically separated from other EBS Volumes, Volumes often share the same physical hardware. Snapshotting the first time forces full block range allocation, so the second snapshot doesn’t need to perform the allocation phase and is faster.
B. The snapshots are incremental so that only the blocks on the device that have changed after your last snapshot are saved in the new snapshot.
C. AWS provisions more disk throughput for burst capacity during snapshots if the drive has been pre-warmed by snapshotting and reading all blocks.
D. The drive is pre-warmed, so block access is more rapid for volumes when every block on the device has already been read at least one time.
After writing data to an EBS volume, you can periodically create a snapshot of the volume to use as a baseline for new volumes or for data backup. If you make periodic snapshots of a volume, the snapshots are incremental so that only the blocks on the device that have changed after your last snapshot are saved in the new snapshot. Even though snapshots are saved incrementally, the snapshot deletion process is designed so that you need to retain only the most recent snapshot in order to restore the volume.
For AWS CloudFormation, which stack state refuses UpdateStack calls?
When a stack is in the UPDATE_ROLLBACK_FAILED state, you can continue rolling it back to return it to a working state (to UPDATE_ROLLBACK_COMPLETE). You cannot update a stack that is in the UPDATE_ROLLBACK_FAILED state. However, if you can continue to roll it back, you can return the stack to its original settings and try to update it again.
You need to migrate 10 million records in one hour into DynamoDB. All records are 1.5KB in size. The data is evenly distributed across the partition key. How many write capacity units should you provision during this batch load?
You need 2 units to make a 1.5KB write, since you round up. You need 20 million total units to perform this load. You have 3600 seconds to do so. Divide and round up for 5556.
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