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Q21. When thinking of AWS OpsWorks, which of the following is not an instance type you can allocate in a stack layer?
A. 24/7 instances
B. Spot instances
C. Time-based instances
D. Load-based instances
Answer: B
Explanation:
AWS OpsWorks supports the following instance types, which are characterized by how they are started and stopped. 24/7 instances are started manually and run until you stop them.Time-based instances are run by AWS OpsWorks on a specified daily and weekly schedule. They allow your stack to automatically adjust the number of instances to accommodate predictable usage patterns. Load-based instances are automatically started and stopped by AWS OpsWorks, based on specified load metrics, such as CPU utilization. They allow your stack to automatically adjust the number of instances to accommodate variations in incoming traffic. Load-based instances are available only for Linux-based stacks. Reference: http://docs.aws.amazon.com/opsworks/latest/userguide/weIcome.htmI
Q22. Which status represents a failure state in AWS CIoudFormation?
A. <code>UPDATE_COMPLETE_CLEANUP_IN_PROGRESS</code>
B. <code>DELETE_COMPLETE_WITH_ARTIFACTS</code>
C. <code>ROLLBACK_IN_PROGRESS</code>
D. <code>ROLLBACK_FAILED</code>
Answer: C
Explanation:
ROLLBACK_IN_PROGRESS means an UpdateStack operation failed and the stack is in the process of trying to return to the valid, pre-update state. UPDATE_COMPLETE_CLEANUP_IN_PROGRESS means an update was successful, and CIoudFormation is deleting any replaced, no longer used resources. ROLLBACK_FA|LED is not a CloudFormation state (but UPDATE_ROLLBACK_FAILED is). DELETE_COMPLETE_W|TH_ART|FACTS does not exist at all.
Reference:
http://docs.aws.amazon.com/AWSCIoudFormation/latest/UserGuide/using-cfn-updating-stacks.html
Q23. 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 fileet of Lambdas to perform record inserts into the RedShift tables. Lambda will scale rapidly enough for the traffic spikes.
B. Use a CIoudFront 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 EMR jobs 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.
Answer: B
Explanation:
Because you only need to batch analyze, anything using streaming is a waste of money. CIoudFront is a Gigabit-Scale HTTP(S) global request distribution service, so it can handle scale, geo-spread, spikes, and unpredictability. The Access Logs will contain the GET data and work just fine for batch analysis and email using EMR.
Can I use Amazon CIoudFront if I expect usage peaks higher than 10 Gbps or 15,000 RPS? Yes. Complete our request for higher limits here, and we will add more capacity to your account within two business days.
Reference: https://aws.amazon.com/Cloudfront/faqs/
Q24. 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: A
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
Q25. 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: B
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
Q26. What is the scope of an EBS volume?
A. VPC
B. Region
C. Placement Group
D. Availability Zone
Answer: D
Explanation:
An Amazon EBS volume is tied to its Availability Zone and can be attached only to instances in the same Availability Zone.
Reference: http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/resources.htmI
Q27. You need to deploy a new application version to production. Because the deployment is high-risk, you need to roll the new version out to users over a number of hours, to make sure everything is working correctly. You need to be able to control the proportion of users seeing the new version of the application down to the percentage point.
You use ELB and EC2 with Auto Scaling Groups and custom AMIs with your code pre-installed assigned to Launch Configurations. There are no database-level changes during your deployment. You have been told you cannot spend too much money, so you must not increase the number of EC2 instances much at all during the deployment, but you also need to be able to switch back to the original version of code quickly if something goes wrong. What is the best way to meet these requirements?
A. Create a second ELB, Auto Scaling Launch Configuration, and Auto Scaling Group using the Launch Configuration. Create AM|s with all code pre-installed. Assign the new AMI to the second Auto Scaling Launch Configuration. Use Route53 Weighted Round Robin Records to adjust the proportion of traffic hitting the two ELBs.
B. Use the Blue-Green deployment method to enable the fastest possible rollback if needed. Create a full second stack of instances and cut the DNS over to the new stack of instances, and change the DNS back if a rollback is needed.
C. Create AMIs with all code pre-installed. Assign the new AMI to the Auto Scaling Launch Configuration, to replace the old one. Gradually terminate instances running the old code (launched with the old Launch Configuration) and allow the new AMIs to boot to adjust the traffic balance to the new code. On rollback, reverse the process by doing the same thing, but changing the AMI on the Launch Config back to the original code.
D. Migrate to use AWS Elastic Beanstalk. Use the established and well-tested Rolling Deployment setting AWS provides on the new Application Environment, publishing a zip bundle of the new code and adjusting the wait period to spread the deployment over time. Re-deploy the old code bundle to rollback if needed.
Answer: A
Explanation:
Only Weighted Round Robin DNS Records and reverse proxies allow such fine-grained tuning of traffic splits. The Blue-Green option does not meet the requirement that we mitigate costs and keep overall EC2 fileet size consistent, so we must select the 2 ELB and ASG option with WRR DNS tuning. This method is called A/B deployment and/or Canary deployment.
Reference: https://d0.awsstatic.com/whitepapers/overview-of-deployment-options-on-aws.pdf
Q28. You need to scale an RDS deployment. You are operating at 10% writes and 90% reads, based on your logging. How best can you scale this in a simple way?
A. Create a second master RDS instance and peer the RDS groups.
B. Cache all the database responses on the read side with CIoudFront.
C. Create read replicas for RDS since the load is mostly reads.
D. Create a Multi-AZ RDS installs and route read traffic to standby.
Answer: C
Explanation:
The high-availability feature is not a scaling solution for read-only scenarios; you cannot use a standby replica to serve read traffic. To service read-only traffic, you should use a Read Replica. For more information, see Working with PostgreSQL, MySQL, and NIariaDB Read Replicas.
Reference: http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.NIuItiAZ.htmI
Q29. Which major database needs a BYO license?
A. PostgreSQL
B. NIariaDB
C. MySQL
D. Oracle
Answer: D
Explanation:
Oracle is not open source, and requires a bring your own license model.
Reference: http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_OracIe.htm|
Q30. 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)seriaIization.
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.
Answer: C
Explanation:
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
Reference: http://docs.aws.amazon.com/generaI/latest/gr/aws_service_Iimits.htm|#|imits_|ambda