Test 1 Cost-Optimized Architectures

A geological research agency maintains the seismological data for the last 100 years. The data has a velocity of 1GB per minute. You would like to store the data with only the most relevant attributes to build a predictive model for earthquakes. What AWS services would you use to build the most cost-effective solution with the LEAST amount of infrastructure maintenance? Ingest the data in Kinesis Data Firehose and use an intermediary Lambda function to filter and transform the incoming stream before the output is dumped on S3 Ingest the data in a Spark Streaming Cluster on EMR use Spark Streaming transformations before writing to S3 Ingest the data in Kinesis Data Streams and use an intermediary Lambda function to filter and transform the incoming stream before the output is dumped on S3 Ingest the data in Kinesis Data Analytics and use SQL queries to filter and transform the data before writing to S3

Explanation Correct option: Ingest the data in Kinesis Data Firehose and use an intermediary Lambda function to filter and transform the incoming stream before the output is dumped on S3 Amazon Kinesis Data Firehose is the easiest way to load streaming data into data stores and analytics tools. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you're already using today. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. It can also batch, compress, and encrypt the data before loading it, minimizing the amount of storage used at the destination and increasing security. Kinesis Data Firehose Overview via - https://aws.amazon.com/kinesis/data-firehose/ The correct option is to ingest the data in Kinesis Data Firehose and use a Lambda function to filter and transform the incoming data before the output is dumped on S3. This way you only need to store a sliced version of the data with only the relevant data attributes required for your model. Also it should be noted that this solution is entirely serverless and requires no infrastructure maintenance. Kinesis Data Firehose to S3: via - https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html Incorrect options: Ingest the data in Kinesis Data Analytics and use SQL queries to filter and transform the data before writing to S3 - Amazon Kinesis Data Analytics is the easiest way to analyze streaming data in real-time. Kinesis Data Analytics enables you to easily and quickly build queries and sophisticated streaming applications in three simple steps: setup your streaming data sources, write your queries or streaming applications, and set up your destination for processed data. Kinesis Data Analytics cannot directly ingest data from the source as it ingests data either from Kinesis Data Streams or Kinesis Data Firehose, so this option is ruled out. Ingest the data in Kinesis Data Streams and use an intermediary Lambda function to filter and transform the incoming stream before the output is dumped on S3 - Amazon Kinesis Data Streams (KDS) is a massively scalable, highly durable data ingestion and processing service optimized for streaming data. Amazon Kinesis Data Streams is integrated with a number of AWS services, including Amazon Kinesis Data Firehose for near real-time transformation. Kinesis Data Streams cannot directly write the output to S3. Unlike Firehose, KDS does not offer a ready-made integration via an intermediary Lambda function to reliably dump data into S3. You will need to do a lot of custom coding to get the Lambda function to process the incoming stream and then store the transformed output to S3 with the constraint that the buffer is maintained reliably and no transformed data is lost. So this option is incorrect. Ingest the data in a Spark Streaming Cluster on EMR use Spark Streaming transformations before writing to S3 - Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. Amazon EMR uses Hadoop, an open-source framework, to distribute your data and processing across a resizable cluster of Amazon EC2 instances. Using an EMR cluster would imply managing the underlying infrastructure so it's ruled out because the correct solution for the given use-case should require the least amount of infrastructure maintenance. Reference: https://aws.amazon.com/kinesis/data-firehose/

A news network uses Amazon S3 to aggregate the raw video footage from its reporting teams across the US. The news network has recently expanded into new geographies in Europe and Asia. The technical teams at the overseas branch offices have reported huge delays in uploading large video files to the destination S3 bucket. Which of the following are the MOST cost-effective options to improve the file upload speed into S3? (Select two) Create multiple site-to-site VPN connections between the AWS Cloud and branch offices in Europe and Asia. Use these VPN connections for faster file uploads into S3 Use multipart uploads for faster file uploads into the destination S3 bucket Use Amazon S3 Transfer Acceleration to enable faster file uploads into the destination S3 bucket Use AWS Global Accelerator for faster file uploads into the destination S3 bucket Create multiple AWS direct connect connections between the AWS Cloud and branch offices in Europe and Asia. Use the direct connect connections for faster file uploads into S3

Explanation Correct options: Use Amazon S3 Transfer Acceleration to enable faster file uploads into the destination S3 bucket - Amazon S3 Transfer Acceleration enables fast, easy, and secure transfers of files over long distances between your client and an S3 bucket. Transfer Acceleration takes advantage of Amazon CloudFront's globally distributed edge locations. As the data arrives at an edge location, data is routed to Amazon S3 over an optimized network path. Use multipart uploads for faster file uploads into the destination S3 bucket - Multipart upload allows you to upload a single object as a set of parts. Each part is a contiguous portion of the object's data. You can upload these object parts independently and in any order. If transmission of any part fails, you can retransmit that part without affecting other parts. After all parts of your object are uploaded, Amazon S3 assembles these parts and creates the object. In general, when your object size reaches 100 MB, you should consider using multipart uploads instead of uploading the object in a single operation. Multipart upload provides improved throughput, therefore it facilitates faster file uploads. Incorrect options: Create multiple AWS direct connect connections between the AWS Cloud and branch offices in Europe and Asia. Use the direct connect connections for faster file uploads into S3 - AWS Direct Connect is a cloud service solution that makes it easy to establish a dedicated network connection from your premises to AWS. AWS Direct Connect lets you establish a dedicated network connection between your network and one of the AWS Direct Connect locations. Direct connect takes significant time (several months) to be provisioned and is an overkill for the given use-case. Create multiple site-to-site VPN connections between the AWS Cloud and branch offices in Europe and Asia. Use these VPN connections for faster file uploads into S3 - AWS Site-to-Site VPN enables you to securely connect your on-premises network or branch office site to your Amazon Virtual Private Cloud (Amazon VPC). You can securely extend your data center or branch office network to the cloud with an AWS Site-to-Site VPN connection. A VPC VPN Connection utilizes IPSec to establish encrypted network connectivity between your intranet and Amazon VPC over the Internet. VPN Connections are a good solution if you have low to modest bandwidth requirements and can tolerate the inherent variability in Internet-based connectivity. Site-to-site VPN will not help in accelerating the file transfer speeds into S3 for the given use-case. Use AWS Global Accelerator for faster file uploads into the destination S3 bucket - AWS Global Accelerator is a service that improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in a single or multiple AWS Regions, such as your Application Load Balancers, Network Load Balancers or Amazon EC2 instances. AWS Global Accelerator will not help in accelerating the file transfer speeds into S3 for the given use-case. References: https://docs.aws.amazon.com/AmazonS3/latest/dev/transfer-acceleration.html https://docs.aws.amazon.com/AmazonS3/latest/dev/uploadobjusingmpu.html

As part of a pilot program, a biotechnology company wants to integrate data files from its on-premises analytical application with AWS Cloud via an NFS interface. Which of the following AWS service is the MOST efficient solution for the given use-case? AWS Storage Gateway - File Gateway AWS Storage Gateway - Tape Gateway AWS Site-to-Site VPN AWS Storage Gateway - Volume Gateway

Explanation Correct option: AWS Storage Gateway - File Gateway AWS Storage Gateway is a hybrid cloud storage service that gives you on-premises access to virtually unlimited cloud storage. The service provides three different types of gateways - Tape Gateway, File Gateway, and Volume Gateway - that seamlessly connect on-premises applications to cloud storage, caching data locally for low-latency access. AWS Storage Gateway's file interface, or file gateway, offers you a seamless way to connect to the cloud in order to store application data files and backup images as durable objects on Amazon S3 cloud storage. File gateway offers SMB or NFS-based access to data in Amazon S3 with local caching. As the company wants to integrate data files from its analytical instruments into AWS via an NFS interface, therefore AWS Storage Gateway - File Gateway is the correct answer. File Gateway Overview: via - https://docs.aws.amazon.com/storagegateway/latest/userguide/StorageGatewayConcepts.html Incorrect options: AWS Storage Gateway - Volume Gateway - You can configure the AWS Storage Gateway service as a Volume Gateway to present cloud-based iSCSI block storage volumes to your on-premises applications. Volume Gateway does not support NFS interface, so this option is not correct. AWS Storage Gateway - Tape Gateway - AWS Storage Gateway - Tape Gateway allows moving tape backups to the cloud. Tape Gateway does not support NFS interface, so this option is not correct. AWS Site-to-Site VPN - AWS Site-to-Site VPN enables you to securely connect your on-premises network or branch office site to your Amazon Virtual Private Cloud (Amazon VPC). You can securely extend your data center or branch office network to the cloud with an AWS Site-to-Site VPN (Site-to-Site VPN) connection. It uses internet protocol security (IPSec) communications to create encrypted VPN tunnels between two locations. You cannot use AWS Site-to-Site VPN to integrate data files via the NFS interface, so this option is not correct. References: https://aws.amazon.com/storagegateway/ https://aws.amazon.com/storagegateway/volume/ https://aws.amazon.com/storagegateway/file/ https://aws.amazon.com/storagegateway/vtl/

An audit department generates and accesses the audit reports only twice in a financial year. The department uses AWS Step Functions to orchestrate the report creating process that has failover and retry scenarios built into the solution. The underlying data to create these audit reports is stored on S3, runs into hundreds of Terabytes and should be available with millisecond latency. As a solutions architect, which is the MOST cost-effective storage class that you would recommend to be used for this use-case? Amazon S3 Intelligent-Tiering (S3 Intelligent-Tiering) Amazon S3 Standard-Infrequent Access (S3 Standard-IA) Amazon S3 Glacier Deep Archive Amazon S3 Standard

Explanation Correct option: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) Since the data is accessed only twice in a financial year but needs rapid access when required, the most cost-effective storage class for this use-case is S3 Standard-IA. S3 Standard-IA storage class is for data that is accessed less frequently but requires rapid access when needed. S3 Standard-IA matches the high durability, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee. Standard-IA is designed for 99.9% availability compared to 99.99% availability of S3 Standard. However, the report creation process has failover and retry scenarios built into the workflow, so in case the data is not available owing to the 99.9% availability of S3 Standard-IA, the job will be auto re-invoked till data is successfully retrieved. Therefore this is the correct option. S3 Storage Classes Overview: via - https://aws.amazon.com/s3/storage-classes/ Incorrect options: Amazon S3 Standard - S3 Standard offers high durability, availability, and performance object storage for frequently accessed data. As described above, S3 Standard-IA storage is a better fit than S3 Standard, hence using S3 standard is ruled out for the given use-case. Amazon S3 Intelligent-Tiering (S3 Intelligent-Tiering) - For a small monthly object monitoring and automation charge, S3 Intelligent-Tiering monitors access patterns and automatically moves objects that have not been accessed to lower-cost access tiers. The S3 Intelligent-Tiering storage class is designed to optimize costs by automatically moving data to the most cost-effective access tier, without performance impact or operational overhead. S3 Standard-IA matches the high durability, high throughput, and low latency of S3 Intelligent-Tiering, with a low per GB storage price and per GB retrieval fee. Moreover, Standard-IA has the same availability as that of S3 Intelligent-Tiering. So, it's cost-efficient to use S3 Standard-IA instead of S3 Intelligent-Tiering. Amazon S3 Glacier Deep Archive - S3 Glacier Deep Archive is a secure, durable, and low-cost storage class for data archiving. S3 Glacier Deep Archive does not support millisecond latency, so this option is ruled out. For more details on the durability, availability, cost and access latency - please review this reference link: https://aws.amazon.com/s3/storage-classes

A retail company has developed a REST API which is deployed in an Auto Scaling group behind an Application Load Balancer. The API stores the user data in DynamoDB and any static content, such as images, are served via S3. On analyzing the usage trends, it is found that 90% of the read requests are for commonly accessed data across all users. As a Solutions Architect, which of the following would you suggest as the MOST efficient solution to improve the application performance? Enable DAX for DynamoDB and ElastiCache Memcached for S3 Enable DynamoDB Accelerator (DAX) for DynamoDB and CloudFront for S3 Enable ElastiCache Redis for DynamoDB and ElastiCache Memcached for S3 Enable ElastiCache Redis for DynamoDB and CloudFront for S3

Explanation Correct option: Enable DynamoDB Accelerator (DAX) for DynamoDB and CloudFront for S3 DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for Amazon DynamoDB that delivers up to a 10 times performance improvement—from milliseconds to microseconds—even at millions of requests per second. DAX is tightly integrated with DynamoDB—you simply provision a DAX cluster, use the DAX client SDK to point your existing DynamoDB API calls at the DAX cluster, and let DAX handle the rest. Because DAX is API-compatible with DynamoDB, you don't have to make any functional application code changes. DAX is used to natively cache DynamoDB reads. CloudFront is a content delivery network (CDN) service that delivers static and dynamic web content, video streams, and APIs around the world, securely and at scale. By design, delivering data out of CloudFront can be more cost-effective than delivering it from S3 directly to your users. When a user requests content that you serve with CloudFront, their request is routed to a nearby Edge Location. If CloudFront has a cached copy of the requested file, CloudFront delivers it to the user, providing a fast (low-latency) response. If the file they've requested isn't yet cached, CloudFront retrieves it from your origin - for example, the S3 bucket where you've stored your content. So, you can use CloudFront to improve application performance to serve static content from S3. Incorrect options: Enable ElastiCache Redis for DynamoDB and CloudFront for S3 Amazon ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications. Amazon ElastiCache for Redis is a great choice for real-time transactional and analytical processing use cases such as caching, chat/messaging, gaming leaderboards, geospatial, machine learning, media streaming, queues, real-time analytics, and session store. ElastiCache for Redis Overview: via - https://aws.amazon.com/elasticache/redis/ Although you can integrate Redis with DynamoDB, it's much more involved than using DAX which is a much better fit. Enable DAX for DynamoDB and ElastiCache Memcached for S3 Enable ElastiCache Redis for DynamoDB and ElastiCache Memcached for S3 Amazon ElastiCache for Memcached is a Memcached-compatible in-memory key-value store service that can be used as a cache or a data store. Amazon ElastiCache for Memcached is a great choice for implementing an in-memory cache to decrease access latency, increase throughput, and ease the load off your relational or NoSQL database. ElastiCache Memcached cannot be used as a cache to serve static content from S3, so both these options are incorrect. References: https://aws.amazon.com/dynamodb/dax/ https://aws.amazon.com/blogs/networking-and-content-delivery/amazon-s3-amazon-cloudfront-a-match-made-in-the-cloud/ https://aws.amazon.com/elasticache/redis/

A media agency stores its re-creatable assets on Amazon S3 buckets. The assets are accessed by a large number of users for the first few days and the frequency of access falls down drastically after a week. Although the assets would be accessed occasionally after the first week, but they must continue to be immediately accessible when required. The cost of maintaining all the assets on S3 storage is turning out to be very expensive and the agency is looking at reducing costs as much as possible. As a Solutions Architect, can you suggest a way to lower the storage costs while fulfilling the business requirements? Configure a lifecycle policy to transition the objects to Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) after 7 days Configure a lifecycle policy to transition the objects to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days Configure a lifecycle policy to transition the objects to Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) after 30 days Configure a lifecycle policy to transition the objects to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 7 days

Explanation Correct option: Configure a lifecycle policy to transition the objects to Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) after 30 days - S3 One Zone-IA is for data that is accessed less frequently, but requires rapid access when needed. Unlike other S3 Storage Classes which store data in a minimum of three Availability Zones (AZs), S3 One Zone-IA stores data in a single AZ and costs 20% less than S3 Standard-IA. S3 One Zone-IA is ideal for customers who want a lower-cost option for infrequently accessed and re-creatable data but do not require the availability and resilience of S3 Standard or S3 Standard-IA. The minimum storage duration is 30 days before you can transition objects from S3 Standard to S3 One Zone-IA. S3 One Zone-IA offers the same high durability, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee. S3 Storage Classes can be configured at the object level, and a single bucket can contain objects stored across S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA, and S3 One Zone-IA. You can also use S3 Lifecycle policies to automatically transition objects between storage classes without any application changes. Constraints for Lifecycle storage class transitions: via - https://docs.aws.amazon.com/AmazonS3/latest/dev/lifecycle-transition-general-considerations.html Supported S3 lifecycle transitions: via - https://docs.aws.amazon.com/AmazonS3/latest/dev/lifecycle-transition-general-considerations.html Incorrect options: Configure a lifecycle policy to transition the objects to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 7 days Configure a lifecycle policy to transition the objects to Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) after 7 days As mentioned earlier, the minimum storage duration is 30 days before you can transition objects from S3 Standard to S3 One Zone-IA or S3 Standard-IA, so both these options are added as distractors. Configure a lifecycle policy to transition the objects to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days - S3 Standard-IA is for data that is accessed less frequently, but requires rapid access when needed. S3 Standard-IA offers the high durability, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee. This combination of low cost and high performance makes S3 Standard-IA ideal for long-term storage, backups, and as a data store for disaster recovery files. But, it costs more than S3 One Zone-IA because of the redundant storage across availability zones. As the data is re-creatable, so you don't need to incur this additional cost. References: https://aws.amazon.com/s3/storage-classes/ https://docs.aws.amazon.com/AmazonS3/latest/dev/lifecycle-transition-general-considerations.html

A financial services company recently launched an initiative to improve the security of its AWS resources and it had enabled AWS Shield Advanced across multiple AWS accounts owned by the company. Upon analysis, the company has found that the costs incurred are much higher than expected. Which of the following would you attribute as the underlying reason for the unexpectedly high costs for AWS Shield Advanced service? AWS Shield Advanced is being used for custom servers, that are not part of AWS Cloud, thereby resulting in increased costs Consolidated billing has not been enabled. All the AWS accounts should fall under a single consolidated billing for the monthly fee to be charged only once AWS Shield Advanced also covers AWS Shield Standard plan, thereby resulting in increased costs Savings Plans has not been enabled for the AWS Shield Advanced service across all the AWS accounts

Explanation Correct option: Consolidated billing has not been enabled. All the AWS accounts should fall under a single consolidated billing for the monthly fee to be charged only once - If your organization has multiple AWS accounts, then you can subscribe multiple AWS Accounts to AWS Shield Advanced by individually enabling it on each account using the AWS Management Console or API. You will pay the monthly fee once as long as the AWS accounts are all under a single consolidated billing, and you own all the AWS accounts and resources in those accounts. Incorrect options: AWS Shield Advanced is being used for custom servers, that are not part of AWS Cloud, thereby resulting in increased costs - AWS Shield Advanced does offer protection to resources outside of AWS. This should not cause unexpected spike in billing costs. AWS Shield Advanced also covers AWS Shield Standard plan, thereby resulting in increased costs - AWS Shield Standard is automatically enabled for all AWS customers at no additional cost. AWS Shield Advanced is an optional paid service. Savings Plans has not been enabled for the AWS Shield Advanced service across all the AWS accounts - This option has been added as a distractor. Savings Plans is a flexible pricing model that offers low prices on EC2, Lambda, and Fargate usage, in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a 1 or 3 year term. Savings Plans is not applicable for the AWS Shield Advanced service. References: https://aws.amazon.com/shield/faqs/ https://aws.amazon.com/savingsplans/faq/

A technology blogger wants to write a review on the comparative pricing for various storage types available on AWS Cloud. The blogger has created a test file of size 1GB with some random data. Next he copies this test file into AWS S3 Standard storage class, provisions an EBS volume (General Purpose SSD (gp2)) with 100GB of provisioned storage and copies the test file into the EBS volume, and lastly copies the test file into an EFS Standard Storage filesystem. At the end of the month, he analyses the bill for costs incurred on the respective storage types for the test file. What is the correct order of the storage charges incurred for the test file on these three storage types? Cost of test file storage on S3 Standard < Cost of test file storage on EBS < Cost of test file storage on EFS Cost of test file storage on EBS < Cost of test file storage on S3 Standard < Cost of test file storage on EFS Cost of test file storage on EFS < Cost of test file storage on S3 Standard < Cost of test file storage on EBS Cost of test file storage on S3 Standard < Cost of test file storage on EFS < Cost of test file storage on EBS

Explanation Correct option: Cost of test file storage on S3 Standard < Cost of test file storage on EFS < Cost of test file storage on EBS With Amazon EFS, you pay only for the resources that you use. The EFS Standard Storage pricing is $0.30 per GB per month. Therefore the cost for storing the test file on EFS is $0.30 for the month. For EBS General Purpose SSD (gp2) volumes, the charges are $0.10 per GB-month of provisioned storage. Therefore, for a provisioned storage of 100GB for this use-case, the monthly cost on EBS is $0.10*100 = $10. This cost is irrespective of how much storage is actually consumed by the test file. For S3 Standard storage, the pricing is $0.023 per GB per month. Therefore, the monthly storage cost on S3 for the test file is $0.023. Therefore this is the correct option. Incorrect options: Cost of test file storage on S3 Standard < Cost of test file storage on EBS < Cost of test file storage on EFS Cost of test file storage on EFS < Cost of test file storage on S3 Standard < Cost of test file storage on EBS Cost of test file storage on EBS < Cost of test file storage on S3 Standard < Cost of test file storage on EFS Following the computations shown earlier in the explanation, these three options are incorrect. References: https://aws.amazon.com/ebs/pricing/ https://aws.amazon.com/s3/pricing/(https://aws.amazon.com/s3/pricing/) https://aws.amazon.com/efs/pricing/

A leading social media analytics company is contemplating moving its dockerized application stack into AWS Cloud. The company is not sure about the pricing for using Elastic Container Service (ECS) with the EC2 launch type compared to the Elastic Container Service (ECS) with the Fargate launch type. Which of the following is correct regarding the pricing for these two services? Both ECS with EC2 launch type and ECS with Fargate launch type are charged based on vCPU and memory resources that the containerized application requests Both ECS with EC2 launch type and ECS with Fargate launch type are charged based on EC2 instances and EBS volumes used Both ECS with EC2 launch type and ECS with Fargate launch type are just charged based on Elastic Container Service used per hour ECS with EC2 launch type is charged based on EC2 instances and EBS volumes used. ECS with Fargate launch type is charged based on vCPU and memory resources that the containerized application requests

Explanation Correct option: ECS with EC2 launch type is charged based on EC2 instances and EBS volumes used. ECS with Fargate launch type is charged based on vCPU and memory resources that the containerized application requests Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service. ECS allows you to easily run, scale, and secure Docker container applications on AWS. ECS Overview: via - https://aws.amazon.com/ecs/ With the Fargate launch type, you pay for the amount of vCPU and memory resources that your containerized application requests. vCPU and memory resources are calculated from the time your container images are pulled until the Amazon ECS Task* terminates, rounded up to the nearest second. With the EC2 launch type, there is no additional charge for the EC2 launch type. You pay for AWS resources (e.g. EC2 instances or EBS volumes) you create to store and run your application. Incorrect options: Both ECS with EC2 launch type and ECS with Fargate launch type are charged based on vCPU and memory resources that the containerized application requests Both ECS with EC2 launch type and ECS with Fargate launch type are charged based on EC2 instances and EBS volumes used As mentioned above - with the Fargate launch type, you pay for the amount of vCPU and memory resources. With EC2 launch type, you pay for AWS resources (e.g. EC2 instances or EBS volumes). Hence both these options are incorrect. Both ECS with EC2 launch type and ECS with Fargate launch type are just charged based on Elastic Container Service used per hour This is a made-up option and has been added as a distractor. References: https://aws.amazon.com/ecs/pricing/

A leading video streaming service delivers billions of hours of content from Amazon S3 to customers around the world. Amazon S3 also serves as the data lake for its big data analytics solution. The data lake has a staging zone where intermediary query results are kept only for 24 hours. These results are also heavily referenced by other parts of the analytics pipeline. Which of the following is the MOST cost-effective strategy for storing this intermediary query data? Store the intermediary query results in S3 Standard-Infrequent Access storage class Store the intermediary query results in S3 One Zone-Infrequent Access storage class Store the intermediary query results in S3 Standard storage class Store the intermediary query results in S3 Glacier Instant Retrieval storage class

Explanation Correct option: Store the intermediary query results in S3 Standard storage class S3 Standard offers high durability, availability, and performance object storage for frequently accessed data. Because it delivers low latency and high throughput, S3 Standard is appropriate for a wide variety of use cases, including cloud applications, dynamic websites, content distribution, mobile and gaming applications, and big data analytics. As there is no minimum storage duration charge and no retrieval fee (remember that intermediary query results are heavily referenced by other parts of the analytics pipeline), this is the MOST cost-effective storage class amongst the given options. Incorrect options: Store the intermediary query results in S3 Glacier Instant Retrieval storage class - S3 Glacier Instant Retrieval delivers the fastest access to archive storage, with the same throughput and milliseconds access as the S3 Standard and S3 Standard-IA storage classes. S3 Glacier Instant Retrieval is ideal for archive data that needs immediate access, such as medical images, news media assets, or user-generated content archives. The minimum storage duration charge is 90 days, so this option is NOT cost-effective because intermediary query results need to be kept only for 24 hours. Hence this option is not correct. Store the intermediary query results in S3 Standard-Infrequent Access storage class - S3 Standard-IA is for data that is accessed less frequently but requires rapid access when needed. S3 Standard-IA offers high durability, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee. This combination of low cost and high performance makes S3 Standard-IA ideal for long-term storage, backups, and as a data store for disaster recovery files. The minimum storage duration charge is 30 days, so this option is NOT cost-effective because intermediary query results need to be kept only for 24 hours. Hence this option is not correct. Store the intermediary query results in S3 One Zone-Infrequent Access storage class - S3 One Zone-IA is for data that is accessed less frequently but requires rapid access when needed. Unlike other S3 Storage Classes which store data in a minimum of three Availability Zones (AZs), S3 One Zone-IA stores data in a single AZ and costs 20% less than S3 Standard-IA. The minimum storage duration charge is 30 days, so this option is NOT cost-effective because intermediary query results need to be kept only for 24 hours. Hence this option is not correct. To summarize again, S3 Standard-IA and S3 One Zone-IA have a minimum storage duration charge of 30 days (so instead of 24 hours, you end up paying for 30 days). S3 Standard-IA and S3 One Zone-IA also have retrieval charges (as the results are heavily referenced by other parts of the analytics pipeline, so the retrieval costs would be pretty high). Therefore, these storage classes are not cost optimal for the given use-case. Reference: https://aws.amazon.com/s3/storage-classes/

A company runs a data processing workflow that takes about 60 minutes to complete. The workflow can withstand disruptions and it can be started and stopped multiple times. Which is the most cost-effective solution to build a solution for the workflow? Use EC2 on-demand instances to run the workflow processes Use EC2 reserved instances to run the workflow processes Use AWS Lambda function to run the workflow processes Use EC2 spot instances to run the workflow processes

Explanation Correct option: Use EC2 spot instances to run the workflow processes EC2 instance types: via - https://aws.amazon.com/ec2/pricing/ Amazon EC2 Spot instances allow you to request spare Amazon EC2 computing capacity for up to 90% off the On-Demand price. Spot instances are recommended for: Applications that have flexible start and end times Applications that are feasible only at very low compute prices Users with urgent computing needs for large amounts of additional capacity For the given use case, spot instances offer the most cost-effective solution as the workflow can withstand disruptions and can be started and stopped multiple times. For example, considering a process that runs for an hour and needs about 1024 MB of memory, spot instance pricing for a t2.micro instance (having 1024 MB of RAM) is $0.0035 per hour. Spot instance pricing: via - https://aws.amazon.com/ec2/spot/pricing/ Contrast this with the pricing of a Lambda function (having 1024 MB of allocated memory), which comes out to $0.0000000167 per 1ms or $0.06 per hour ($0.0000000167 * 1000 * 60 * 60 per hour). Lambda function pricing: via - https://aws.amazon.com/lambda/pricing/ Thus, a spot instance turns out to be about 20 times cost effective than a Lambda function to meet the requirements of the given use case. Incorrect options: Use AWS Lambda function to run the workflow processes - As mentioned in the explanation above, a Lambda function turns out to be 20 times more expensive than a spot instance to meet the workflow requirements of the given use case, so this option is incorrect. You should also note that the maximum execution time of a Lambda function is 15 minutes, so the workflow process would be disrupted for sure. On the other hand, it is certainly possible that the workflow process can be completed in a single run on the spot instance (the average frequency of stop instance interruption across all Regions and instance types is <10%). Use EC2 on-demand instances to run the workflow processes Use EC2 reserved instances to run the workflow processes You should note that both on-demand and reserved instances are more expensive than spot instances. In addition, reserved instances have a term of 1 year or 3 years, so they are not suited for the given workflow. Therefore, both these options are incorrect. References: https://aws.amazon.com/ec2/pricing/ https://aws.amazon.com/ec2/spot/pricing/ https://aws.amazon.com/lambda/pricing/ https://aws.amazon.com/ec2/spot/instance-advisor/

A company has a web application that runs 24*7 in the production environment. The development team at the company runs a clone of the same application in the dev environment for up to 8 hours every day. The company wants to build the MOST cost-optimal solution by deploying these applications using the best-fit pricing options for EC2 instances. What would you recommend? Use reserved EC2 instances for the production application and spot block instances for the dev application Use reserved EC2 instances for the production application and spot instances for the dev application Use on-demand EC2 instances for the production application and spot instances for the dev application Use reserved EC2 instances for the production application and on-demand instances for the dev application

Explanation Correct option: Use reserved EC2 instances for the production application and on-demand instances for the dev application There are multiple pricing options for EC2 instances, such as On-Demand, Savings Plans, Reserved Instances, and Spot Instances. EC2 Instances Pricing Options: via - https://aws.amazon.com/ec2/pricing/ Amazon EC2 Reserved Instances (RI) provide a significant discount (up to 72%) compared to On-Demand pricing and provide a capacity reservation when used in a specific Availability Zone. RIs provide you with a significant discount (up to 72%) compared to On-Demand instance pricing. You have the flexibility to change families, OS types, and tenancies while benefitting from RI pricing when you use Convertible RIs. via - https://aws.amazon.com/ec2/pricing/ For the given use case, you can use reserved EC2 instances for the production application as it is run 24*7. This way you can get a 72% discount if you avail a 3-year term. You can use on-demand instances for the dev application since it is only used for up to 8 hours per day. On-demand offers the flexibility to only pay for the EC2 instance when it is being used (0 to 8 hours for the given use case). Incorrect options: Use reserved EC2 instances for the production application and spot block instances for the dev application - Spot blocks can only be used for a span of up to 6 hours, so this option does not meet the requirements of the given use case where the dev application can be up and running up to 8 hours. You should also note that AWS has stopped offering Spot blocks to new customers. Use reserved EC2 instances for the production application and spot instances for the dev application Use on-demand EC2 instances for the production application and spot instances for the dev application Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various stateless, fault-tolerant, or flexible applications. via - https://aws.amazon.com/ec2/spot/ Spot instances can be taken back by AWS with two minutes of notice, so spot instances cannot be reliably used for running the dev application (which can be up and running for up to 8 hours). So both these options are incorrect. References: https://aws.amazon.com/ec2/pricing/ https://aws.amazon.com/blogs/aws/new-ec2-spot-blocks-for-defined-duration-workloads/ https://aws.amazon.com/ec2/spot/

The IT department at a consulting firm is conducting a training workshop for new developers. As part of an evaluation exercise on Amazon S3, the new developers were asked to identify the invalid storage class lifecycle transitions for objects stored on S3. Can you spot the INVALID lifecycle transitions from the options below? (Select two) S3 Intelligent-Tiering => S3 Standard S3 Standard-IA => S3 One Zone-IA S3 Standard-IA => S3 Intelligent-Tiering S3 One Zone-IA => S3 Standard-IA S3 Standard => S3 Intelligent-Tiering

Explanation Correct options: As the question wants to know about the INVALID lifecycle transitions, the following options are the correct answers - S3 Intelligent-Tiering => S3 Standard S3 One Zone-IA => S3 Standard-IA Following are the unsupported life cycle transitions for S3 storage classes - Any storage class to the S3 Standard storage class. Any storage class to the Reduced Redundancy storage class. The S3 Intelligent-Tiering storage class to the S3 Standard-IA storage class. The S3 One Zone-IA storage class to the S3 Standard-IA or S3 Intelligent-Tiering storage classes. Incorrect options: S3 Standard => S3 Intelligent-Tiering S3 Standard-IA => S3 Intelligent-Tiering S3 Standard-IA => S3 One Zone-IA Here are the supported life cycle transitions for S3 storage classes - The S3 Standard storage class to any other storage class. Any storage class to the S3 Glacier or S3 Glacier Deep Archive storage classes. The S3 Standard-IA storage class to the S3 Intelligent-Tiering or S3 One Zone-IA storage classes. The S3 Intelligent-Tiering storage class to the S3 One Zone-IA storage class. The S3 Glacier storage class to the S3 Glacier Deep Archive storage class. Amazon S3 supports a waterfall model for transitioning between storage classes, as shown in the diagram below. via - https://docs.aws.amazon.com/AmazonS3/latest/dev/lifecycle-transition-general-considerations.html Reference: https://docs.aws.amazon.com/AmazonS3/latest/dev/lifecycle-transition-general-considerations.html

A video analytics organization has been acquired by a leading media company. The analytics organization has 10 independent applications with an on-premises data footprint of about 70TB for each application. The CTO of the media company has set a timeline of two weeks to carry out the data migration from on-premises data center to AWS Cloud and establish connectivity. Which of the following are the MOST cost-effective options for completing the data transfer and establishing connectivity? (Select two) Order 1 Snowmobile to complete the one-time data transfer Order 70 Snowball Edge Storage Optimized devices to complete the one-time data transfer Setup AWS direct connect to establish connectivity between the on-premises data center and AWS Cloud Setup Site-to-Site VPN to establish on-going connectivity between the on-premises data center and AWS Cloud Order 10 Snowball Edge Storage Optimized devices to complete the one-time data transfer

Explanation Correct options: Order 10 Snowball Edge Storage Optimized devices to complete the one-time data transfer Snowball Edge Storage Optimized is the optimal choice if you need to securely and quickly transfer dozens of terabytes to petabytes of data to AWS. It provides up to 80 TB of usable HDD storage, 40 vCPUs, 1 TB of SATA SSD storage, and up to 40 Gb network connectivity to address large scale data transfer and pre-processing use cases. As each Snowball Edge Storage Optimized device can handle 80TB of data, you can order 10 such devices to take care of the data transfer for all applications. Exam Alert: The original Snowball devices were transitioned out of service and Snowball Edge Storage Optimized are now the primary devices used for data transfer. You may see the Snowball device on the exam, just remember that the original Snowball device had 80TB of storage space. Setup Site-to-Site VPN to establish on-going connectivity between the on-premises data center and AWS Cloud AWS Site-to-Site VPN enables you to securely connect your on-premises network or branch office site to your Amazon Virtual Private Cloud (Amazon VPC). You can securely extend your data center or branch office network to the cloud with an AWS Site-to-Site VPN connection. A VPC VPN Connection utilizes IPSec to establish encrypted network connectivity between your intranet and Amazon VPC over the Internet. VPN Connections can be configured in minutes and are a good solution if you have an immediate need, have low to modest bandwidth requirements, and can tolerate the inherent variability in Internet-based connectivity. Therefore this option is the right fit for the given use-case as the connectivity can be easily established within the given timeframe. Incorrect options: Order 1 Snowmobile to complete the one-time data transfer - Each Snowmobile has a total capacity of up to 100 petabytes. To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. So Snowmobile is not the right fit for this use-case. Setup AWS direct connect to establish connectivity between the on-premises data center and AWS Cloud - AWS Direct Connect lets you establish a dedicated network connection between your network and one of the AWS Direct Connect locations. Using industry-standard 802.1q VLANs, this dedicated connection can be partitioned into multiple virtual interfaces. AWS Direct Connect does not involve the Internet; instead, it uses dedicated, private network connections between your intranet and Amazon VPC. Direct Connect involves significant monetary investment and takes at least a month to set up, therefore it's not the correct fit for this use-case. Order 70 Snowball Edge Storage Optimized devices to complete the one-time data transfer - As the data-transfer can be completed with just 10 Snowball Edge Storage Optimized devices, there is no need to order 70 devices. References: https://aws.amazon.com/snowball/faqs/ https://aws.amazon.com/vpn/ https://aws.amazon.com/snowmobile/faqs/ https://aws.amazon.com/directconnect/