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Q11. You are hired as the new head of operations for a SaaS company. Your CTO has asked you to make debugging any part of your entire operation simpler and as fast as possible. She complains that she has   no idea what is going on in the complex, service-oriented architecture, because the developers just log to disk, and it's very hard to find errors in logs on so many services. How can you best meet this requirement and satisfy your CTO?

A. Copy all log files into AWS S3 using a cron job on each instance. Use an S3 Notification Configuration on the <code>PutBucket</code> event and publish events to AWS Lambda. Use the Lambda to analyze logs as soon as they come in and flag issues.

B. Begin using CIoudWatch Logs on every service. Stream all Log Groups into S3 objects. Use AWS EMR clusterjobs to perform ad-hoc MapReduce analysis and write new queries when needed.

C. Copy all log files into AWS S3 using a cron job on each instance. Use an S3 Notification Configuration on the <code>PutBucket</code> event and publish events to AWS Kinesis. Use Apache Spark on AWS EMR to perform at-scale stream processing queries on the log chunks and flag issues.

D. Begin using CIoudWatch Logs on every service. Stream all Log Groups into an AWS Elasticsearch Service Domain running Kibana 4 and perform log analysis on a search cluster.

Answer:

Explanation:

The Elasticsearch and Kibana 4 combination is called the ELK Stack, and is designed specifically for real-time, ad-hoc log analysis and aggregation. All other answers introduce extra delay or require pre-defined queries.

Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud. Elasticsearch is a popular open-source search and analytics engine for use cases such as log analytics, real-time application monitoring, and click stream analytics.    Reference:     https://aws.amazon.com/elasticsearch-service/

Q12. What is the order of most-to-least rapidly-scaling (fastest to scale first)?

A. EC2 + ELB + Auto Scaling B. Lambda C. RDS

A. B, A, C

B. C, B, A

C. C, A, B

D. A, C, B

Answer:

Explanation:

Lambda is designed to scale instantly. EC2 + ELB + Auto Scaling require single-digit minutes to scale out. RDS will take atleast 15 minutes, and will apply OS patches or any other updates when applied.  Reference: https://aws.amazon.com/|ambda/faqs/

Q13. Which of these is not an instrinsic function in AWS CloudFormation?

A. Fn::EquaIs

B. Fn::|f

C. Fn::Not

D. Fn::Parse 

Answer: D

Explanation:

This is the complete list of Intrinsic Functions…: Fn::Base64, Fn::And, Fn::EquaIs, Fn::If, Fn::Not, Fn::Or, Fn::FindInMap, Fn::GetAtt, Fn::GetAZs, Fn::Join, Fn::Se|ect, Ref

Reference:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-reference.html

Q14. Your API requires the ability to stay online during AWS regional failures. Your API does not store any state, it only aggregates data from other sources – you do not have a database. What is a simple but effective way to achieve this uptime goal?

A. Use a CloudFront distribution to serve up your API. Even if the region your API is in goes down, the edge locations CIoudFront uses will be fine.

B. Use an ELB and a cross-zone ELB deployment to create redundancy across datacenters. Even if a region fails, the other AZ will stay online.

C. Create a Route53 Weighted Round Robin record, and if one region goes down, have that region redirect to the other region.

D. Create a Route53 Latency Based Routing Record with Failover and point it to two identical deployments of your stateless API in two different regions. Make sure both regions use Auto Scaling Groups behind ELBs.

Answer:

Explanation:

Latency Based Records allow request distribution when all is well with both regions, and the Failover component enables fallbacks between regions. By adding in the ELB and ASG, your system in the survMng region can expand to meet 100% of demand instead of the original fraction, whenever failover occurs.

Reference:       http://docs.aws.amazon.com/Route53/Iatest/DeveIoperGuide/dns-failover.html

You are designing an enterprise data storage system. Your data management software system requires mountable disks and a real filesystem, so you cannot use S3 for storage. You need persistence, so you will be using AWS EBS Volumes for your system. The system needs as low-cost storage as possible, and access is not frequent or high throughput, and is mostly sequential reads. Which is the most appropriate EBS Volume Type for this scenario?

A. gpl

B. iol

C. standard

D. gp2 

Answer: C

Explanation:

standard volumes, or Magnetic volumes, are best for: Cold workloads where data is infrequently accessed, or scenarios where the lowest storage cost is important.

Reference:       http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSVoIumeTypes.htmI

Q15. What is required to achieve gigabit network throughput on EC2? You already selected cluster-compute, 10GB instances with enhanced networking, and your workload is already network-bound, but you are not seeing 10 gigabit speeds.

A. Enable biplex networking on your servers, so packets are non-blocking in both directions and there's no switching overhead.

B. Ensure the instances are in different VPCs so you don't saturate the Internet Gateway on any one VPC.

C. Select PIOPS for your drives and mount several, so you can provision sufficient disk throughput.

D. Use a placement group for your instances so the instances are physically near each other in the same Availability Zone.

Answer:

Explanation:

You are not guaranteed 10gigabit performance, except within a placement group.

A placement group is a logical grouping of instances within a single Availability Zone. Using placement groups enables applications to participate in a low-latency, 10 Gbps network. Placement groups are recommended for applications that benefit from low network latency, high network throughput, or both. Reference:       http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html

Q16. You need to create an audit log of all changes to customer banking data. You use DynamoDB to store this customer banking data. |t's important not to lose any information due to server failures. What is an elegant way to accomplish this?

A. Use a DynamoDB StreamSpecification and stream all changes to AWS Lambda. Log the changes to

AWS CIoudWatch Logs, removing sensitive information before logging.

B. Before writing to DynamoDB, do a pre-write acknoledgment to disk on the application sewer, removing sensitive information before logging. Periodically rotate these log files into S3.

C. Use a DynamoDB StreamSpecification and periodically flush to an EC2 instance store, removing sensitive information before putting the objects. Periodically flush these batches to S3.

D. Before writing to DynamoDB, do a pre-write acknoledgment to disk on the application sewer, removing sensitive information before logging. Periodically pipe these files into CloudWatch Logs.

Answer:

Explanation:

All suggested periodic options are sensitive to sewer failure during or between periodic flushes.   Streaming to Lambda and then logging to CIoudWatch Logs will make the system resilient to instance and Availability Zone failures.

Reference:      http://docs.aws.amazon.com/Iambda/latest/dg/with-ddb.html

Q17. You want to pass queue messages that are 1GB each. How should you achieve this?

A. Use Kinesis as a buffer stream for message bodies. Store the checkpoint id for the placement in the Kinesis Stream in SQS.

B. Use the Amazon SQS Extended Client Library for Java and Amazon S3 as a storage mechanism for message bodies.

C. Use SQS's support for message partitioning and multi-part uploads on Amazon S3.

D. Use AWS EFS as a shared pool storage medium. Store filesystem pointers to the files on disk in the SQS message bodies.

Answer:

Explanation:

You can manage Amazon SQS messages with Amazon S3. This is especially useful for storing and retrieving messages with a message size of up to 2 GB. To manage Amazon SQS messages with Amazon S3, use the Amazon SQS Extended Client Library for Java.

Reference:

http://docs.aws.amazon.com/AWSSimpIeQueueService/latest/SQSDeveIoperGuide/s3-messages.html

Q18. 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 CIoudWatch 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 CIoudWatch Metrics + Billing Alarm + Lambda event subscription. When a threshold is exceeded, email the manager.

D. Scrape the billing page periodically and publish to SNS. 

Answer: C

Explanation:

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. Reference:        http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/free-tier-aIarms.htmI

Q19. For AWS Auto Scaling, what is the first transition state a new instance enters after leaving steady state when scaling out due to increased load?

A. EnteringStandby

B. Pending

C. Terminating:Wait

D. Detaching 

Answer: B

Explanation:

When a scale out event occurs, the Auto Scaling group launches the required number of EC2 instances, using its assigned launch configuration. These instances start in the Pending state. If you add a lifecycle hook to your Auto Scaling group, you can perform a custom action here. For more information, see Lifecycle Hooks.

Reference: http://docs.aws.amazon.com/AutoScaling/latest/DeveIoperGuide/AutoScaIingGroupLifecycIe.html

Q20. There are a number of ways to purchase compute capacity on AWS. Which orders the price per compute or memory unit from LOW to HIGH (cheapest to most expensive), on average?

A. On-Demand B. Spot C. Reserved

A. A, B, C

B. C, B, A

C. B, C, A

D. A, C, B

Answer:

Explanation:

Spot instances are usually many, many times cheaper than on-demand prices. Reserved instances, depending on their term and utilization, can yield approximately 33% to 66% cost savings. On-Demand prices are the baseline price and are the most expensive way to purchase EC2 compute time.    Reference:       https://d0.awsstatic.com/whitepapers/Cost_Optimization_with_AWS.pdf

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