A company is implementing a shared storage solution for a media application that is hosted m the AWS Cloud The company needs the ability to use SMB clients to access data The solution must he fully managed.
Which AWS solution meets these requirements?

• A.Create an AWS Storage Gateway volume gateway. Create a file share that uses the required client protocol Connect the application server to the file share.
• B.Create an AWS Storage Gateway tape gateway Configure (apes to use Amazon S3 Connect the application server lo the tape gateway
• C.Create an Amazon EC2 Windows instance Install and configure a Windows file share role on the instance. Connect the application server to the file share.
• D.Create an Amazon FSx for Windows File Server tile system Attach the fie system to the origin server.
  Connect the application server to the file system

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The application must respond to DNS queries from the secondary Region if the primary Region fails. Only one Region must serve traffic at a time.
Which solution will meet these requirements?

• A.Create an outbound endpoint in Amazon Route 53 Resolver. Create forwarding rules that determine how queries will be forwarded to DNS resolvers on the network. Associate the rules with VPCs in each Region.
• B.Create primary and secondary DNS records in Amazon Route 53. Configure health checks and a failover routing policy.
• C.Create a traffic policy in Amazon Route 53. Use a geolocation routing policy and a value type of ELB Application Load Balancer.
• D.Create an Amazon Route 53 profile. Associate DNS resources to the profile. Associate the profile with VPCs in each Region.

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A company wishes to query data that resides in multiple AWS accounts from a central data lake. Each account has its own Amazon S3 bucket that stores data unique to its business function. Users from different accounts must be granted access to the data lake based on their roles.
Which solution will minimize overhead and costs while meeting the required access patterns?

• A.Use AWS Kinesis Firehose to consolidate data from multiple accounts into a single account.
• B.Use AWS Central Tower to centrally manage each account's S3 buckets.
• C.Create a scheduled Lambda function for transferring data from multiple accounts to the S3 buckets of a central account
• D.Use AWS Lake Formation to consolidate data from multiple accounts into a single account.

Correct Answer: D

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake enables you to break down data silos and combine different types of analytics to gain insights and guide better business decisions.
Amazon S3 forms the storage layer for Lake Formation. If you already use S3, you typically begin by registering existing S3 buckets that contain your data. Lake Formation creates new buckets for the data lake and import data into them. AWS always stores this data in your account, and only you have direct access to it.


AWS Lake Formation is integrated with AWS Glue which you can use to create a data catalog that describes available datasets and their appropriate business applications. Lake Formation lets you define policies and control data access with simple "grant and revoke permissions to data" sets at granular levels. You can assign permissions to IAM users, roles, groups, and Active Directory users using federation. You specify permissions on catalog objects (like tables and columns) rather than on buckets and objects.
Thus, the correct answer is: Use AWS Lake Formation to consolidate data from multiple accounts into a single account.
The option that says: Use AWS Kinesis Firehose to consolidate data from multiple accounts into a single account is incorrect. Setting up a Kinesis Firehose in each and every account to move data into a single location is costly and impractical. A better approach is to set up cross-account sharing which is free with AWS Lake Formation.
The option that says: Create a scheduled Lambda function for transferring data from multiple accounts to the S3 buckets of a central account is incorrect. This could be done by utilizing the AWS SDK, but implementation would be difficult and quite challenging to manage. Remember that the scenario explicitly mentioned that the solution must minimize management overhead.
The option that says: Use AWS Central Tower to centrally manage each account's S3 buckets is incorrect because the AWS Central Tower service is primarily used to manage and govern multiple AWS accounts and not just S3 buckets. Using the AWS Lake Formation service is a more suitable choice.
References:
https://aws.amazon.com/blogs/big-data/building-securing-and-managing-data-lakes-with-aws-lake-forma tion/
https://docs.aws.amazon.com/lake-formation/latest/dg/how-it-works.html

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A video game company is deploying a new gaming application to its global users. The company requires a solution that will provide near real-time reviews and rankings of the players.
A solutions architect must design a solution to provide fast access to the dat a. The solution must also ensure the data persists on disks in the event that the company restarts the application.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Configure an Amazon CloudFront distribution with an Amazon S3 bucket as the origin. Store the player data in the S3 bucket.
• B.Create Amazon EC2 instances in multiple AWS Regions. Store the player data on the EC2 instances. Configure Amazon Route 53 with geolocation records to direct users to the closest EC2 instance.
• C.Deploy an Amazon ElastiCache for Redis cluster. Store the player data in the ElastiCache cluster.
• D.Deploy an Amazon ElastiCache for Memcached cluster. Store the player data in the ElastiCache cluster.

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A company is building a solution that will report Amazon EC2 Auto Scaling events across all the applications in an AWS account. The company needs to use a serverless solution to store the EC2 Auto Scaling status data in Amazon S3. The company then will use the data in Amazon S3 to provide near-real-time updates in a dashboard. The solution must not affect the speed of EC2 instance launches.
How should the company move the data to Amazon S3 to meet these requirements?

• A.Use an Amazon CloudWatch metric stream to send the EC2 Auto Scaling status data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.
• B.Launch an Amazon EMR cluster to collect the EC2 Auto Scaling status data and send the data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.
• C.Create an Amazon EventBridge rule to invoke an AWS Lambda function on a schedule. Configure the Lambda function to send the EC2 Auto Scaling status data directly to Amazon S3.
• D.Use a bootstrap script during the launch of an EC2 instance to install Amazon Kinesis Agent. Configure Kinesis Agent to collect the EC2 Auto Scaling status data and send the data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.

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