Few Important points to remember :

default : Your instance runs on shared hardware.

dedicated : Your instance runs on single-tenant hardware. But more instances from single ac can utilized the same dedicated H/w.

host : Your instance runs on a Dedicated Host, which is an isolated server.

After you launch an instance, there are some limitations to changing its tenancy.

You cannot change the tenancy of an instance from default to dedicated or host after you've launched it.

You cannot change the tenancy of an instance from dedicated or host to default after you've launched it.

But you can do change the tenancy of an instance from dedicated to host, or from host to dedicated after you've launched it. No need to create separate AMI.

Message timers in Amazon SQS allow you to specify a delay for individual messages. This feature is perfect for scenarios where certain messages need to be postponed for a specific period while others need to be delivered immediately. By setting a message timer, you can delay the delivery of specific messages to the queue by the required time, such as one minute in this case.

Why the other answers are wrong:

A. Use visibility timeout to postpone the delivery of certain messages to the queue by one minute: Visibility timeout controls the time a message is invisible after a reader picks it up from the queue, not the delay before it is delivered to the queue.

B. Use dead-letter queues to postpone the delivery of certain messages to the queue by one minute: Dead-letter queues are used for handling messages that fail to be processed successfully after a certain number of attempts, not for delaying message delivery.

C. Use delay queues to postpone the delivery of certain messages to the queue by one minute: Delay queues apply a uniform delay to all messages in the queue, not selectively to certain messages.

Example:

The data analytics company can set a message timer for specific messages that need to be delayed by one minute when they send them to the SQS queue. For instance, using the AWS SDK, they can specify the DelaySeconds parameter in the SendMessage API call to set the delay for individual messages. This approach ensures that only the targeted messages are delayed, while others are delivered immediately.

Target Tracking Scaling Policy: This policy automatically adjusts the number of EC2 instances in the Auto Scaling group to maintain a specified metric at a target value. It works similarly to a thermostat, making it ideal for handling sudden and unpredictable spikes in workload.

Example: Suppose the target tracking policy is configured to maintain the SQS queue length at an average of 50 messages. When a spike in orders increases the queue length, the Auto Scaling group will add more EC2 instances to process the messages, maintaining the performance of the application.

Why Other Options Are Less Suitable:

A. Step Scaling Policy: This involves defining specific thresholds for scaling actions (e.g., add 2 instances if the queue length exceeds 100 messages). It's less responsive to sudden changes and can be harder to fine-tune compared to target tracking.

C. Simple Scaling Policy: Similar to step scaling but with a single scaling action per alarm. It lacks the flexibility and responsiveness needed for handling sudden spikes efficiently.

D. Scheduled Scaling Policy: This scales resources based on a predefined schedule. It does not account for sudden, unpredictable spikes in workload, making it unsuitable for this use case.

Using a target tracking scaling policy is the most effective way to ensure the application can handle sudden spikes in orders without performance degradation.

Inbound Endpoint (Option B): This allows DNS queries from the on-premises network to be forwarded to the Route 53 Resolver in the AWS VPC. This is crucial for resolving AWS VPC resources from the on-premises network.

Example: When an on-premises server needs to resolve an AWS VPC resource, it can send the DNS query to the inbound endpoint on the Route 53 Resolver.

Outbound Endpoint (Option E): This allows the Route 53 Resolver in the AWS VPC to forward DNS queries to the on-premises network's DNS resolvers. This is essential for resolving on-premises resources from the AWS VPC.

Example: When an AWS instance needs to resolve an on-premises resource, the Route 53 Resolver can send the DNS query to the outbound endpoint, which then forwards it to the on-premises DNS resolver.

Why Other Options Are Less Suitable:

A. Outbound Endpoint Only: This alone won't enable the on-premises network to resolve AWS VPC resources.

C. Inbound Endpoint Only: This alone won't enable the AWS VPC to resolve on-premises resources.

D. Universal Endpoint: There is no such concept as a "universal endpoint" in Route 53 Resolver.

Combining inbound and outbound endpoints ensures bi-directional DNS resolution between the AWS VPC and the on-premises network, providing a complete solution for the company's requirements.

DynamoDB is a fully managed NoSQL database service offered by AWS. It provides fast and predictable performance with seamless scalability, making it ideal for real-time applications with high traffic volumes. DynamoDB also supports in-memory caching through DynamoDB Accelerator (DAX), further reducing latency for read-heavy workloads.

Here's why the other options are not suitable:

DocumentDB: While DocumentDB is a NoSQL database, it is optimized for document-based data models and might not offer the same level of performance and scalability as DynamoDB for real-time use cases.

ElastiCache: ElastiCache is an in-memory caching service and doesn't provide persistent storage, which is a requirement for this use case.

RDS: RDS is a managed relational database service and is not designed for the high-performance requirements of real-time media streaming.

Example:

The media streaming company can use DynamoDB to store metadata about videos, user preferences, and viewing history. DynamoDB's low latency and elastic scalability ensure a smooth streaming experience even during peak usage times. They can leverage DynamoDB DAX to cache frequently accessed data, further improving response times for critical real-time operations like video playback and content recommendations.

NAT Gateway in Public Subnets: NAT gateways are used to allow instances in private subnets to connect to the internet or other AWS services, but prevent the internet from initiating connections with those instances. By placing a NAT gateway in each public subnet, you ensure high availability and redundancy across all Availability Zones.

Example: If you have a NAT gateway in each public subnet (one per AZ), EC2 instances in private subnets can route their internet-bound traffic to the NAT gateway, which will then forward the traffic to the internet.

Why Other Options Are Less Suitable:

A. Egress-Only Internet Gateways: These are used for IPv6 traffic, not IPv4. Since the VPC and subnets use IPv4 CIDR blocks, egress-only internet gateways are not applicable.

B. Internet Gateways in Private Subnets: Internet gateways should be in public subnets, not private subnets. Also, each VPC should have a single internet gateway, not one per AZ.

C. NAT Gateways in Private Subnets: NAT gateways need to be in public subnets to provide internet access. If they are in private subnets, they won't have internet access themselves.

Setting up NAT gateways in the public subnets ensures that instances in private subnets can access the internet securely and efficiently.

Correct option:

AWS Step Functions

AWS Step Functions lets you coordinate and orchestrate multiple AWS services such as AWS Lambda and AWS Glue into serverless workflows. Workflows are made up of a series of steps, with the output of one step acting as input into the next. A Step Function automatically triggers and tracks each step, and retries when there are errors, so your application executes in order and as expected. The Step Function can ensure that the Glue ETL job and the lambda functions execute in order and complete successfully as per the workflow defined in the given use-case. Therefore, Step Function is the best solution.

How Step Functions Work : https://aws.amazon.com/step-functions/

Incorrect options:

AWS Batch - AWS Batch is a set of batch management capabilities that enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. AWS Batch cannot be used to orchestrate a workflow, hence it is an incorrect option.

Amazon Simple Workflow Service (SWF) - Amazon SWF helps developers build, run, and scale background jobs that have parallel or sequential steps. In Amazon SWF, tasks represent invocations of logical steps in applications. Tasks are processed by workers which are programs that interact with Amazon SWF to get tasks, process them, and return their results. To coordinate the application execution across workers, you write a program called the decider in your choice of programming language. Although Amazon SWF provides you complete control over your orchestration logic, it increases the complexity of developing applications. Hence this option is not correct.

Exam Alert:

Please understand the differences between Amazon SWF vs. AWS Step Functions

https://aws.amazon.com/swf/faqs/

Amazon EMR - 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. EMR cannot be used to orchestrate a workflow, hence it is an incorrect option.

References:

https://aws.amazon.com/step-functions/

https://aws.amazon.com/swf/faqs/

Correct option:

Provisioned IOPS SSD (io1)

Provisioned IOPS SSD (io1) is backed by solid-state drives (SSDs) and is a high-performance EBS storage option designed for critical, I/O intensive database and application workloads, as well as throughput-intensive database workloads. io1 is designed to deliver a consistent baseline performance of up to 50 IOPS/GB to a maximum of 64,000 IOPS and provide up to 1,000 MB/s of throughput per volume. Therefore, the io1 volume type would be able to meet the requirement of 25,000 IOPS per volume for the given use-case.

Incorrect options:

General Purpose SSD (gp2) - gp2 is backed by solid-state drives (SSDs) and is suitable for a broad range of transactional workloads, including dev/test environments, low-latency interactive applications, and boot volumes. It supports max IOPS/Volume of 16,000.

Cold HDD (sc1) - sc1 is backed by hard disk drives (HDDs). It is ideal for less frequently accessed workloads with large, cold datasets. It supports max IOPS/Volume of 250.

Throughput Optimized HDD (st1) - st1 is backed by hard disk drives (HDDs) and is ideal for frequently accessed, throughput-intensive workloads with large datasets and large I/O sizes, such as MapReduce, Kafka, log processing, data warehouse, and ETL workloads. It supports max IOPS/Volume of 500.

Reference:

https://aws.amazon.com/ebs/volume-types/

Amazon SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS acts as a buffer, allowing microservices to communicate asynchronously, thus ensuring that slower processes do not block faster ones.

Here's why the other options are not as suitable:

B. Amazon EventBridge: While EventBridge is a serverless event bus that can be used for decoupling, it's primarily designed for event-driven architectures and might not be the most efficient choice for handling significant differences in processing speeds.

C. Amazon Kinesis Data Streams: Kinesis is designed for real-time streaming of large amounts of data and is not ideal for simple message passing between microservices.

D. Amazon SNS: SNS is a pub/sub messaging service and is better suited for scenarios where you need to fan out messages to multiple subscribers, rather than managing different processing speeds.

Example:

Consider a microservice architecture for an e-commerce application:

Fast microservice: Handles tasks like adding items to a cart, which require quick responses.

Slow microservice: Handles tasks like processing payments, which might take longer due to external dependencies.

By using SQS, the fast microservice can send a message to the queue and continue processing other requests. The slow microservice can then retrieve the message from the queue and process it at its own pace, without impacting the performance of the faster microservice.

Correct option:

Host the static content on Amazon S3 and use Lambda with DynamoDB for the serverless web application that handles dynamic content. Amazon CloudFront will sit in front of Lambda for distribution across diverse regions - Lambda with DynamoDB is the right answer for a serverless solution. CloudFront will help in enhancing user experience by delivering content, across different geographic locations with low latency. Amazon S3 is a cost-effective and faster way of distributing static content for web applications.

Incorrect options:

Host both the static and dynamic content of the web application on Amazon S3 and use Amazon CloudFront for distribution across diverse regions/countries - S3 is not the right fit for hosting Dynamic content, so this option is incorrect.

Host the static content on Amazon S3 and use Amazon EC2 with RDS for generating the dynamic content. Amazon CloudFront can be configured in front of EC2 instance, to make global distribution easy - The company is looking for a serverless solution, and Amazon EC2 is not a serverless service as the EC2 instances have to be managed by AWS customers.

Host both the static and dynamic content of the web application on Amazon EC2 with RDS as the database. Amazon CloudFront should be configured to distribute the content across geographically disperse regions - This is a possible solution, but not a cost-effective or optimal one. Since static content can be cost-effectively managed on Amazon S3 and can be accessed and distributed faster when compared to fetching the content from the EC2 server.

Reference:

https://aws.amazon.com/blogs/networking-and-content-delivery/deliver-your-apps-dynamic-content-using-amazon-cloudfront-getting-started-template/