Amazon AWS Certified Solutions Architect - Associate SAA-C03 Practice Test Questions, Exam Dumps

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AWS Certified Solutions Architect - Associate SAA-C03 Amazon Practice Test Questions and Exam Dumps

Question No 1:

A company collects temperature, humidity, and atmospheric pressure data from multiple cities across different continents. The average volume of data collected daily from each site is 500 GB. Each site has a high-speed Internet connection. The company wants to aggregate this data from all global sites and store it in a single Amazon S3 bucket as quickly as possible while minimizing operational complexity.

Which solution meets these requirements?

A. Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.
B. Upload the data from each site to an S3 bucket in the closest Region. Use S3 Cross-Region Replication to copy objects to the destination S3 bucket. Then remove the data from the origin S3 bucket.
C. Schedule AWS Snowball Edge Storage Optimized device jobs daily to transfer data from each site to the closest Region. Use S3 Cross-Region Replication to copy objects to the destination S3 bucket.
D. Upload the data from each site to an Amazon EC2 instance in the closest Region. Store the data in an Amazon Elastic Block Store (Amazon EBS) volume. At regular intervals, take an EBS snapshot and copy it to the Region that contains the destination S3 bucket. Restore the EBS volume in that Region.

Answer: A. Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.

Explanation:

The company's goal is to aggregate data from multiple global sites into a single Amazon S3 bucket as quickly as possible, with minimal operational complexity. Let's break down each option and explain why Option A is the best choice:

Option A: S3 Transfer Acceleration with Multipart Uploads

S3 Transfer Acceleration speeds up the upload of large objects to an Amazon S3 bucket by using Amazon CloudFront's globally distributed edge locations. When enabled, data is routed to the nearest edge location, significantly improving upload speeds, especially for large files or large volumes of data across long distances.
Multipart Uploads allow data to be uploaded in parts in parallel, further optimizing performance. This solution minimizes the time required to transfer large amounts of data from each site to S3 and reduces the operational overhead, as everything is uploaded directly to the destination S3 bucket.
Operational Simplicity: Enabling Transfer Acceleration and using multipart uploads involves minimal configuration and no additional infrastructure, making this a simple and highly effective solution to meet the company’s requirements.

Option B: Cross-Region Replication

While this option might work for aggregating data into a single S3 bucket, it introduces additional complexity and delays in data transfer. Uploading the data first to an S3 bucket in the closest Region and then using Cross-Region Replication to copy the objects to the destination S3 bucket adds more steps, requires additional configuration, and may increase latency. This is not the most efficient or straightforward solution.

Option C: AWS Snowball Edge

AWS Snowball Edge is a physical device used for large-scale data transfer when there is limited internet bandwidth. However, it is more suited for situations where high-speed Internet is unavailable or when large data transfers are needed without relying on network bandwidth. In this case, the company has high-speed Internet at each site, making Snowball unnecessary and overly complex.

Option D: EC2 and EBS Snapshot Method

This method involves uploading data to an Amazon EC2 instance, storing it in EBS volumes, and then copying EBS snapshots to the destination Region. This approach introduces significant complexity in managing EC2 instances, EBS volumes, and snapshots. It also introduces unnecessary steps and overhead when compared to a direct upload to S3, making it more operationally complex and inefficient for the company’s needs.

The most efficient, simple, and fast solution to aggregate data into Amazon S3 is to use S3 Transfer Acceleration combined with multipart uploads (Option A). This method directly uploads data from the sites to the destination S3 bucket, minimizing transfer times and operational complexity.

Question No 2:

A company needs the ability to analyze the log files of its proprietary application. The logs are stored in JSON format in an Amazon S3 bucket. The company needs to run simple queries on these logs on-demand. The solutions architect must provide a solution that minimizes changes to the existing architecture while also minimizing operational overhead.

What should the solutions architect do to meet these requirements with the least amount of operational overhead?

A. Use Amazon Redshift to load all the content into one place and run the SQL queries as needed.
B. Use Amazon CloudWatch Logs to store the logs. Run SQL queries as needed from the Amazon CloudWatch console.
C. Use Amazon Athena directly with Amazon S3 to run the queries as needed.
D. Use AWS Glue to catalog the logs. Use a transient Apache Spark cluster on Amazon EMR to run the SQL queries as needed.

Answer: C. Use Amazon Athena directly with Amazon S3 to run the queries as needed.

Explanation:

The company wants to analyze log files stored in Amazon S3 with minimal changes to the existing architecture and minimal operational overhead. Let’s evaluate each solution:

Option A: Use Amazon Redshift

Amazon Redshift is a data warehouse service that is ideal for running complex analytics over large datasets. However, using Redshift would require loading the log data into the data warehouse, which would involve additional data migration, storage costs, and management overhead. Given that the company only needs to run simple queries on the log files, Redshift introduces unnecessary complexity and overhead for this use case.

Option B: Use Amazon CloudWatch Logs

Amazon CloudWatch Logs is useful for collecting and monitoring logs, but it is not optimized for on-demand querying of logs stored in S3, especially when the logs are in JSON format. While CloudWatch allows querying logs, it requires more configuration and would introduce unnecessary complexity in managing log storage. Additionally, CloudWatch is not as suited for querying large datasets directly stored in S3 as Amazon Athena.

Option C: Use Amazon Athena

Amazon Athena is a serverless interactive query service that allows you to run SQL-like queries on data directly stored in Amazon S3. It supports JSON files and does not require moving or duplicating data, making it the most efficient solution for the company’s needs. Athena can be configured quickly to query the logs in real-time, and since it is serverless, there is no infrastructure management required. This solution directly addresses the company’s requirements with the least operational overhead, as the logs can remain in their current S3 bucket without needing to be moved or transformed.

Option D: Use AWS Glue and Apache Spark on Amazon EMR

AWS Glue is a data cataloging service, and Amazon EMR can be used to run big data applications like Apache Spark. However, this approach requires setting up an EMR cluster and managing the infrastructure, which is complex and introduces unnecessary overhead, especially when the company only needs simple on-demand queries on JSON logs stored in S3. Using AWS Glue and Spark would overcomplicate the solution compared to using Athena.

The simplest, most cost-effective, and scalable solution to meet the company’s requirements is to use Amazon Athena (Option C). Athena allows the company to run SQL queries directly on the logs stored in Amazon S3, without requiring data migration, additional infrastructure, or complex configuration. This minimizes operational overhead and is the best fit for on-demand querying of JSON log files.

Question no 3:

A company uses AWS Organizations to manage multiple AWS accounts for different departments. The management account has an Amazon S3 bucket that contains project reports. The company wants to limit access to this S3 bucket to only users from accounts within the same AWS Organization.

Which solution meets these requirements with the least amount of operational overhead?

A. Add the aws:PrincipalOrgID global condition key with a reference to the organization ID to the S3 bucket policy.
B. Create an organizational unit (OU) for each department. Add the aws:PrincipalOrgPaths global condition key to the S3 bucket policy.
C. Use AWS CloudTrail to monitor the CreateAccount, InviteAccountToOrganization, LeaveOrganization, and RemoveAccountFromOrganization events. Update the S3 bucket policy accordingly.
D. Tag each user that needs access to the S3 bucket. Add the aws:PrincipalTag global condition key to the S3 bucket policy.

Answer: A. Add the aws:PrincipalOrgID global condition key with a reference to the organization ID to the S3 bucket policy.

Explanation:

To meet the requirement of restricting access to an Amazon S3 bucket to only users from accounts within a specific AWS Organization, the solution must utilize an organizational condition key in the S3 bucket policy.

Option A:

The best solution here is to use the aws:PrincipalOrgID condition key in the S3 bucket policy. This condition allows you to restrict access to the S3 bucket to only those requests originating from accounts that belong to a specified AWS Organization.
aws:PrincipalOrgID is a global condition key that can be added to the S3 bucket policy with a reference to the organization ID of the AWS Organization. This solution requires minimal operational overhead as it is straightforward to implement and doesn't require managing tags or complex infrastructure setups.
This solution is scalable and doesn't require frequent updates, making it the most efficient choice.

Option B:

aws:PrincipalOrgPaths is a valid condition key, but it is more granular and typically used for restricting access to accounts based on the organizational unit (OU) and paths. While it can be used, it adds complexity to the solution, as it requires additional setup of OUs and is not as simple as using aws:PrincipalOrgID for broader organization-based access.

Option C:

Using AWS CloudTrail to monitor account events (like CreateAccount or LeaveOrganization) does not directly provide a solution to controlling access to the S3 bucket. CloudTrail is used for logging and monitoring AWS API calls, but it does not automate the process of controlling access based on organizational membership. This option introduces unnecessary complexity and overhead, as the S3 bucket policy would have to be updated manually based on CloudTrail events.

Option D:

Tagging each user and using aws:PrincipalTag is not the most efficient approach in this scenario. Tagging can be useful in some cases, but it would require significant management of user tags, which could introduce operational overhead, especially as the organization grows and changes. It is less scalable than using the aws:PrincipalOrgID condition key.

The simplest and most effective way to restrict S3 access to users in the same AWS Organization is by using the aws:PrincipalOrgID condition in the S3 bucket policy (Option A). It is the most scalable, minimal-effort solution with the least operational overhead.

Question No 4:

An application runs on an Amazon EC2 instance in a VPC. The application processes logs that are stored in an Amazon S3 bucket. The EC2 instance needs to access the S3 bucket without connectivity to the internet.

Which solution will provide private network connectivity to Amazon S3?

A. Create a gateway VPC endpoint to the S3 bucket.
B. Stream the logs to Amazon CloudWatch Logs. Export the logs to the S3 bucket.
C. Create an instance profile on Amazon EC2 to allow S3 access.
D. Create an Amazon API Gateway API with a private link to access the S3 endpoint.

Answer: A. Create a gateway VPC endpoint to the S3 bucket.

Explanation:

In this scenario, the EC2 instance needs to access Amazon S3 without connecting to the internet. The solution must ensure private connectivity to S3 within the VPC.

Option A:

The best solution is to create a VPC endpoint specifically for Amazon S3. This is known as a gateway VPC endpoint. It enables private connectivity between the EC2 instance in the VPC and the S3 bucket, without needing an internet connection or a NAT gateway. The VPC endpoint routes traffic directly to the S3 bucket over the AWS private network, keeping the traffic internal to AWS, which enhances security and avoids internet egress costs.
This solution is simple, secure, and ensures that S3 access remains private within the VPC.

Option B:

Streaming logs to CloudWatch Logs and then exporting them to the S3 bucket is an indirect solution. While CloudWatch can be used for log processing, it does not provide a direct and private connection to S3. This adds unnecessary complexity and does not address the requirement for private network connectivity to S3.

Option C:

Creating an instance profile with the appropriate IAM permissions allows the EC2 instance to access the S3 bucket. However, this only manages permissions and does not address the network connectivity requirement. The EC2 instance would still need internet connectivity unless you implement a VPC endpoint or NAT solution.

Option D:

Using API Gateway and a private link is a more complex and non-standard approach for accessing S3. API Gateway is typically used for creating APIs and is not the ideal method for accessing S3 from within a VPC. It introduces unnecessary complexity compared to a gateway VPC endpoint.

The most efficient and secure solution is to create a gateway VPC endpoint (Option A). This ensures private network connectivity between the EC2 instance and S3 without the need for internet access.

Question No 5:

A company is hosting a web application on AWS using a single Amazon EC2 instance that stores user-uploaded documents in an Amazon EBS volume. For better scalability and availability, the company duplicated the architecture and created a second EC2 instance and EBS volume in another Availability Zone, placing both behind an Application Load Balancer. After completing this change, users reported that each time they refreshed the website, they could see one subset of their documents or the other, but never all of the documents at the same time.

What should a solutions architect propose to ensure users see all of their documents at once?

A. Copy the data so both EBS volumes contain all the documents.
B. Configure the Application Load Balancer to direct a user to the server with the documents.
C. Copy the data from both EBS volumes to Amazon EFS. Modify the application to save new documents to Amazon EFS.
D. Configure the Application Load Balancer to send the request to both servers. Return each document from the correct server.

Answer: C. Copy the data from both EBS volumes to Amazon EFS. Modify the application to save new documents to Amazon EFS.

Explanation:

The issue here is that the Application Load Balancer is distributing traffic between two EC2 instances, each with its own EBS volume. Since the documents are only stored on one EBS volume, users see different sets of documents depending on which instance handles their request.

Option A:

Simply copying the data between the two EBS volumes does not solve the problem of synchronization and file consistency between the two EC2 instances. This method can quickly become difficult to manage, especially as the application grows.

Option B:

Configuring the Application Load Balancer to direct a user to the server with the relevant documents will result in a situation where only one set of documents is visible to the user. This solution does not guarantee that users will always see all their documents.

Option C:

The best solution is to use Amazon EFS (Elastic File System), a managed file storage service that can be mounted across multiple EC2 instances. By moving the documents to Amazon EFS, both EC2 instances can access the same set of files, ensuring that users will always see all of their documents regardless of which EC2 instance handles their request.
This solution is scalable, ensures data consistency across instances, and is easy to implement with minimal changes to the application.

Option D:

Configuring the Application Load Balancer to send requests to both servers and return documents from the correct server is not a practical solution. It would still result in partial visibility of documents, as the data is stored on separate EBS volumes.

The best solution is to migrate the data to Amazon EFS (Option C), ensuring that both EC2 instances can access the same set of documents. This ensures consistency and visibility for all users.

Question No 6:

A company uses NFS to store large video files on an on-premises network attached storage (NAS). The video files range from 1 MB to 500 GB each, and the total storage is 70 TB. The company decides to migrate these video files to Amazon S3 and needs to do so as quickly as possible while minimizing network bandwidth usage.

Which solution will meet these requirements?

A. Create an S3 bucket. Create an IAM role that has permissions to write to the S3 bucket. Use the AWS CLI to copy all files locally to the S3 bucket.
B. Create an AWS Snowball Edge job. Receive a Snowball Edge device on premises. Use the Snowball Edge client to transfer data to the device. Return the device so that AWS can import the data into Amazon S3.
C. Deploy an S3 File Gateway on premises. Create a public service endpoint to connect to the S3 File Gateway. Create an S3 bucket. Create a new NFS file share on the S3 File Gateway. Point the new file share to the S3 bucket. Transfer the data from the existing NFS file share to the S3 File Gateway.
D. Set up an AWS Direct Connect connection between the on-premises network and AWS. Deploy an S3 File Gateway on premises. Create a public virtual interface (VIF) to connect to the S3 File Gateway. Create an S3 bucket. Create a new NFS file share on the S3 File Gateway. Point the new file share to the S3 bucket. Transfer the data from the existing NFS file share to the S3 File Gateway.

Answer: B. Create an AWS Snowball Edge job. Receive a Snowball Edge device on premises. Use the Snowball Edge client to transfer data to the device. Return the device so that AWS can import the data into Amazon S3.

Explanation:

In this scenario, the company needs to migrate 70 TB of large video files to Amazon S3 as quickly as possible while minimizing network bandwidth usage. The best solution involves physically transporting the data to AWS using the AWS Snowball Edge service.

Option B is the best choice because:

AWS Snowball Edge is a physical device designed to transfer large amounts of data to AWS quickly and securely. It is ideal for situations where transferring data over the internet would be slow, costly, or not feasible.
The company can use the Snowball Edge client to load the data from the on-premises NFS storage to the device, then ship the device to AWS, where the data will be directly uploaded to the specified S3 bucket. This method drastically reduces the load on the network and minimizes bandwidth usage.
The Snowball Edge solution can handle large volumes of data and provides a fast and reliable way to complete the migration with minimal operational overhead.

Option A (using the AWS CLI) is not ideal because:

Transferring 70 TB over the network using the CLI would consume significant bandwidth and may take too long, especially if the network is not optimized for such a large transfer.

Option C (using S3 File Gateway) could work, but it is designed for ongoing file sharing, not large initial migrations. This would still require significant network bandwidth for the data transfer and may not be as efficient as the Snowball solution.

Option D (using AWS Direct Connect) is a more complex solution that involves setting up a dedicated network connection to AWS. While it can increase bandwidth, it would still involve transferring the data over the network, making it less optimal compared to using Snowball Edge.

Using AWS Snowball Edge (Option B) is the most efficient and practical solution for migrating large video files to Amazon S3 quickly while minimizing the impact on network bandwidth.

Question No 7:

A company has an application that ingests incoming messages. Dozens of other applications and microservices quickly consume these messages. The number of messages varies drastically and sometimes increases suddenly to 100,000 messages per second. The company wants to decouple the solution and increase scalability.

Which solution meets these requirements?

A. Persist the messages to Amazon Kinesis Data Analytics. Configure the consumer applications to read and process the messages.
B. Deploy the ingestion application on Amazon EC2 instances in an Auto Scaling group to scale the number of EC2 instances based on CPU metrics.
C. Write the messages to Amazon Kinesis Data Streams with a single shard. Use an AWS Lambda function to preprocess messages and store them in Amazon DynamoDB. Configure the consumer applications to read from DynamoDB to process the messages.
D. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SQS) subscriptions. Configure the consumer applications to process the messages from the queues.

Answer: D. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SQS) subscriptions. Configure the consumer applications to process the messages from the queues.

Explanation:

The company wants to decouple the message processing and scale the solution to handle the dynamic message throughput of up to 100,000 messages per second.

Option D is the best choice because:

Amazon SNS is designed to fan out messages to multiple subscribers. Using SNS with SQS ensures that the solution can scale horizontally and decouple message ingestion from processing. Each consumer application can subscribe to the SNS topic and read the messages from its own SQS queue. This decouples the producers from the consumers, making it highly scalable.
SQS queues are durable and provide message ordering and at-least-once delivery guarantees. By scaling the number of queues or using SQS FIFO queues, the solution can handle the large volume of messages.

Option A (using Kinesis Data Analytics) is not the best choice because Kinesis is more suited for streaming analytics and real-time processing of large volumes of data, rather than simple message ingestion and decoupling.

Option B (using EC2 instances) is not scalable or efficient. It requires manually managing the EC2 instances and handling spikes in traffic through CPU-based auto scaling, which is less effective and more complex compared to an event-driven approach with SNS and SQS.

Option C (using Kinesis Data Streams and DynamoDB) would work but may introduce unnecessary complexity. Writing to DynamoDB adds an extra step, and Kinesis with a single shard may not handle the high volume of messages efficiently.

SNS with SQS (Option D) provides the best solution to decouple the message ingestion and processing, while ensuring scalability to handle large and varying message volumes.

Question No 8:

A company is migrating a distributed application to AWS. The application serves variable workloads. The legacy platform consists of a primary server that coordinates jobs across multiple compute nodes. The company wants to modernize the application with a solution that maximizes resiliency and scalability.

How should a solutions architect design the architecture to meet these requirements?

A. Configure an Amazon Simple Queue Service (Amazon SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling to use scheduled scaling.
B. Configure an Amazon Simple Queue Service (Amazon SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling based on the size of the queue.
C. Implement the primary server and the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure AWS CloudTrail as a destination for the jobs. Configure EC2 Auto Scaling based on the load on the primary server.
D. Implement the primary server and the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure Amazon EventBridge (Amazon CloudWatch Events) as a destination for the jobs. Configure EC2 Auto Scaling based on the load on the compute nodes.

Answer: B. Configure an Amazon Simple Queue Service (Amazon SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling based on the size of the queue.

Explanation:

In this scenario, the company is migrating a distributed application to AWS and needs to modernize the architecture for resiliency and scalability. The best approach is to use SQS to decouple job submission from processing, allowing the compute nodes to scale based on the job load.

Option B is the best choice because:

Amazon SQS acts as a message queue to store the jobs. The compute nodes, which are EC2 instances in an Auto Scaling group, can scale dynamically based on the number of jobs in the queue. This approach maximizes scalability and resiliency because the system can adjust to varying loads and automatically scale to handle job processing efficiently.
Using Auto Scaling based on the queue size ensures that the compute capacity is directly tied to demand, optimizing resource usage.

Option A (using scheduled scaling) is not ideal because scheduled scaling does not respond to real-time fluctuations in workload. It would be less efficient in handling sudden spikes in workload.

Option C (using CloudTrail) is not appropriate because CloudTrail is used for auditing and tracking AWS API calls, not for job coordination or workload scaling.

Option D (using EventBridge) is not the best fit because EventBridge is more suited for event-driven workflows that trigger

Question No 9:

A company is running an SMB file server in its data center. The file server stores large files that are frequently accessed for the first few days after they are created. After 7 days, the files are rarely accessed. The total data size is increasing and is approaching the company’s total storage capacity. A solutions architect must find a way to increase the company’s available storage space while maintaining low-latency access to the most recently accessed files. Additionally, the architect must provide file lifecycle management to avoid future storage capacity issues.

Which solution will meet these requirements?

A. Use AWS DataSync to copy data that is older than 7 days from the SMB file server to AWS.
B. Create an Amazon S3 File Gateway to extend the company’s storage space. Create an S3 Lifecycle policy to transition the data to S3 Glacier Deep Archive after 7 days.
C. Create an Amazon FSx for Windows File Server file system to extend the company’s storage space.
D. Install a utility on each user’s computer to access Amazon S3. Create an S3 Lifecycle policy to transition the data to S3 Glacier Flexible Retrieval after 7 days.

Answer: B. Create an Amazon S3 File Gateway to extend the company’s storage space. Create an S3 Lifecycle policy to transition the data to S3 Glacier Deep Archive after 7 days.

Explanation:

In this scenario, the company needs to expand its storage capacity while managing data access in a way that allows low-latency access to recently created files and cost-effective storage for older, rarely accessed data.

Option B is the best solution because:

Amazon S3 File Gateway provides an effective way to extend storage while keeping local access to files. It acts as a hybrid storage solution by allowing you to maintain on-premises access to files while moving data to Amazon S3.
S3 Glacier Deep Archive is the most cost-effective option for storing infrequently accessed data after the files have aged past 7 days. The S3 Lifecycle policy automates the process of transitioning data that is older than 7 days to Glacier Deep Archive, which ensures that storage costs are minimized while retaining long-term data storage capabilities.
This solution optimizes storage costs and ensures that frequently accessed files remain in Amazon S3, which offers low-latency access. The transition to Glacier Deep Archive after 7 days automatically manages the lifecycle of files based on access patterns.

Option A (using AWS DataSync) would be effective for data transfer but lacks the automated lifecycle management needed to efficiently transition data to lower-cost storage tiers. DataSync is more appropriate for one-time or periodic migrations, not ongoing data lifecycle management.

Option C (using Amazon FSx for Windows File Server) is a good solution for Windows file sharing but does not provide cost-effective storage options for infrequently accessed files. It also doesn't have a built-in mechanism for transitioning old data to lower-cost tiers like Glacier.

Option D (installing a utility to access S3 from each user's computer) introduces unnecessary complexity and requires users to have specific software installed. Also, it doesn't support the automatic lifecycle management that S3 File Gateway provides.

Amazon S3 File Gateway (Option B) combined with S3 Lifecycle policies provides an efficient solution to manage both recent and infrequently accessed files, ensuring cost-effective storage with minimal operational overhead.

Question No 10:

A company is building an ecommerce web application on AWS. The application sends information about new orders to an Amazon API Gateway REST API for processing. The company wants to ensure that the orders are processed in the order they are received, maintaining the sequence in which the requests were made.

Which solution will meet these requirements?

A. Use an API Gateway integration to publish a message to an Amazon Simple Notification Service (Amazon SNS) topic when the application receives an order. Subscribe an AWS Lambda function to the topic to perform processing.
B. Use an API Gateway integration to send a message to an Amazon Simple Queue Service (Amazon SQS) FIFO queue when the application receives an order. Configure the SQS FIFO queue to invoke an AWS Lambda function for processing.
C. Use an API Gateway authorizer to block any requests while the application processes an order.
D. Use an API Gateway integration to send a message to an Amazon Simple Queue Service (Amazon SQS) standard queue when the application receives an order. Configure the SQS standard queue to invoke an AWS Lambda function for processing.

Answer: B. Use an API Gateway integration to send a message to an Amazon Simple Queue Service (Amazon SQS) FIFO queue when the application receives an order. Configure the SQS FIFO queue to invoke an AWS Lambda function for processing.

Explanation:

In this scenario, the company wants to ensure that the orders are processed in sequence, as they are received by the API Gateway. The solution must guarantee that the order of processing matches the order of receipt.

Option B is the correct choice because:

Amazon SQS FIFO queues are designed to ensure that messages are processed in the exact order they are sent. This is crucial for applications like the ecommerce application where processing must happen in a specific sequence (i.e., processing the first order before the second).
The API Gateway sends the order details to the SQS FIFO queue, which ensures that each order is processed in the order it was received. The queue then triggers an AWS Lambda function to process each order in the exact sequence.
SQS FIFO queues also support deduplication to ensure that no duplicate messages are processed, making the system more reliable.

Option A (using SNS) does not guarantee message order, as SNS is designed for broadcasting messages to multiple subscribers, and does not ensure the sequence in which messages are delivered.

Option C (using an API Gateway authorizer) is unrelated to message ordering. An authorizer is used for controlling access, not for managing message sequencing.

Option D (using SQS standard queue) would not guarantee the order of processing. Standard queues in SQS do not maintain message order, which could lead to inconsistent processing of orders.

SQS FIFO queues (Option B) ensure that the orders are processed in the exact order they are received, providing reliable, sequential processing while integrating seamlessly with AWS Lambda for processing each order.