The GetSessionToken API call returns a set of temporary credentials for an AWS account or IAM user. The credentials consist of an access key ID, a secret access key, and a security token. Generally, you use GetSessionToken if you want to use MFA to protect programmatic calls to specific AWS API operations. Therefore, the Developer can use GetSessionToken with an MFA device to make secure API calls using the AWS SDK.

DecodeAuthorizationMessage decodes additional information about the authorization status of a request from an encoded message returned in response to an AWS request.

GetCallerIdentity action returns details about the IAM user or role whose credentials are used to call the operation.

GetFederationToken is used with federated users to return a set of temporary security credentials (consisting of an access key ID, a secret access key, and a security token).

1. "IAM user credentials used by the application" & only option GetSessionToken & GetFederationToken are able to return security credentials. 2. "multi-factor authentication protected APIs" which is a general practice with GetSessionToken.

AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals.

Amazon SNS and Amazon SQS are used together when we want messages to be delivered to applications that require immediate notification of an event, and also persisted in an Amazon SQS queue for other applications to process at a later time and neither of this will help to manage the state machine.

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 makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters. With EMR you can run petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. You can run workloads on Amazon EC2 instances, on Amazon Elastic Kubernetes Service (EKS) clusters, or on-premises using EMR on AWS Outposts.

AWS Step Functions is a serverless function orchestrator that makes it easy to sequence AWS Lambda functions and multiple AWS services into business-critical applications. Through its visual interface, you can create and run a series of checkpointed and event-driven workflows that maintain the application state. The output of one step acts as an input to the next. Each step in your application executes in order, as defined by your business logic.

1."Lambda functions invoking one another" clearly indicates towards AWS Step Functions, as it is used to manage Lambda Functions, invoke one another.

You should not include an external library within the function code. Even if possible this would result in bloated code that could slow down execution time.

This would likely result in increased latency of our function execution. Instead we should either package the library in the deployment package for our function or use layers.

You can configure your Lambda function to pull in additional code and content in the form of layers. A layer is a ZIP archive that contains libraries, a custom runtime, or other dependencies. With layers, you can use libraries in your function without needing to include them in your deployment package. Layers let you keep your deployment package small, which makes development easier. One of the best practices for AWS Lambda functions is to minimize your deployment package size to its runtime necessities in order to reduce the amount of time that it takes for your deployment package to be downloaded and unpacked ahead of invocation. Therefore, it is preferable to use layers to store the external libraries to optimize performance of the function.

Best practice is to minimize package sizes to runtime necessities. Also, this would require including the library in all function deployment packages which is more time consuming & increase package size whereas with layers we can create a single layer that is used by all functions.

1."using an external library" & to include external library, best practice is to use configuration-layers in Lambda.