Data Pipeline is a service used for data migration, not deploying updates to applications.

AWS Data Pipeline is a Managed ETL Service that helps you process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. With AWS Data Pipeline, you can regularly access your data where it’s stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.

AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages that are ready to deploy. 

The requirement is not to build the application, it is to deploy the application.

You can specify one or more deployment groups for a CodeDeploy application. Each application deployment uses one of its deployment groups. The deployment group contains settings and configurations used during the deployment.

You can associate more than one deployment group with an application in CodeDeploy. This makes it possible to deploy an application revision to different sets of instances at different times. For example, you might use one deployment group to deploy an application revision to a set of instances tagged Test where you ensure the quality of the code.

Next, you deploy the same application revision to a deployment group with instances tagged Staging for additional verification. Finally, when you are ready to release the latest application to customers, you deploy to a deployment group that includes instances tagged Production.

Therefore, using AWS CodeDeploy to create multiple deployment groups can be used to meet the requirement

AWS CodeCommit is a secure, highly scalable, managed source control service that hosts private Git repositories.

The requirement is to deploy the same code to separate environments in a staged manner. Therefore, having multiple code repositories is not useful.

1." deploy its code first " - which clearly indicates towards CodeDeploy.

The required Resources section declares the AWS resources that you want to include in the stack, such as an Amazon EC2 instance or an Amazon S3 bucket.

The optional Outputs section declares output values that you can import into other stacks (to create cross-stack references), return in response (to describe stack calls) or view on the AWS CloudFormation console.

Parameters enable you to input custom values to your template each time you create or update a stack.

The optional Mappings section matches a key to a corresponding set of named values. For example, if you want to set values based on a region, you can create a mapping that uses the region name as a key and contains the values you want to specify for each specific region. You use the Fn::FindInMap intrinsic function to retrieve values in a map.

The following example shows a Mappings section with a map RegionMap, which contains five keys that map to name-value pairs containing single string values. The keys are region names. Each name-value pair is the AMI ID for the HVM64 AMI in the region represented by the key.

1." different Amazon Machine Image (AMI) in each region." for which we have to use Mappings as it matches a key to a corresponding set of named values.

With Amazon DynamoDB transactions, you can group multiple actions together and submit them as a single all-or-nothing TransactWriteItems or TransactGetItems operation.

• A TransactWriteItems operation differs from an BatchWriteItem operation in that all the actions it contains must be completed successfully, or no changes are made at all.
• With a BatchWriteItem operation, it is possible that only some of the actions in the batch succeed while the others do not.

So, this is not fulfilling our requirement of retrieval of the latest data

DynamoDB DAX caches items from DynamoDB to improve read performance but will not ensure the latest data is retrieved.

DynamoDB supports eventually consistent and strongly consistent reads. When using eventually consistent reads the response might not reflect the results of a recently completed write operation. The response might include some stale data.
When using strongly consistent reads DynamoDB returns a response with the most up-to-date data, reflecting the updates from all prior write operations that were successful.
DynamoDB uses eventually consistent reads unless you specify otherwise. Read operations (such as GetItem, Query, and Scan) provide a ConsistentRead parameter. If you set this parameter to true, DynamoDB uses strongly consistent reads during the operation.

A global secondary index contains a selection of attributes from the base table, but they are organized by a primary key that is different from that of the table. The index key does not need to have any of the key attributes from the table. It doesn't even need to have the same key schema as a table.

A GSI does not assist in any way in this solution.

1. "most up-to-date data is retrieved for all queries" indicates DynamoDb should use strongly consistent reads, for which we have to set the ConsistentRead parameter to true when calling Read Operations(such as GetItem, Query, and Scan).