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Amazon SAA-C03 (Amazon AWS Certified Solutions Architect - Associate) exam is a certification exam designed by Amazon Web Services (AWS) to test the knowledge and skills of individuals in designing and deploying scalable, highly available, and fault-tolerant systems on AWS. AWS Certified Solutions Architect - Associate certification is intended for individuals who have experience in designing and deploying applications on AWS, as well as those who are interested in learning about various AWS services and their best practices.

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To prepare for the Amazon SAA-C03 Exam, candidates are advised to familiarize themselves with the AWS platform and its core services. They should also have a good understanding of cloud computing concepts, such as virtualization, storage, and networking. There are many resources available online to help candidates prepare for the exam, including AWS documentation, training courses, and practice exams.

Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q200-Q205):

NEW QUESTION # 200
A company launches a new web application that uses an Amazon Aurora PostgreSQL database. The company wants to add new features to the application that rely on AI. The company requires vector storage capability to use AI tools.
Which solution will meet this requirement MOST cost-effectively?

A. Install the pgvector extension on the Aurora PostgreSQL database. Configure the application to write vector embeddings to a vector table.
B. Create an Amazon Neptune ML cluster. Configure the application to write vector embeddings to a vector graph.
C. Use Amazon OpenSearch Service to create an OpenSearch service. Configure the application to write vector embeddings to a vector index.
D. Create an Amazon DocumentDB cluster. Configure the application to write vector embeddings to a vector index.

Answer: A

Explanation:
Aurora PostgreSQL supports the pgvector extension, which allows storage and querying of vector embeddings directly inside the database. This eliminates the need for external vector databases and provides cost-effective and performant integration for AI workloads.
Reference: AWS Documentation - Amazon Aurora PostgreSQL and pgvector Support

NEW QUESTION # 201
A company wants to run its payment application on AWS The application receives payment notifications from mobile devices Payment notifications require a basic validation before they are sent for further processing The backend processing application is long running and requires compute and memory to be adjusted The company does not want to manage the infrastructure Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon Simple Queue Service (Amazon SQS) queue Integrate the queue with an Amazon EventBndge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere Create a standalone cluster
B. Create an Amazon API Gateway API Integrate the API with AWS Lambda to receive payment notifications from mobile devices Invoke a Lambda function to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Container Service (Amazon ECS). Configure Amazon ECS with an AWS Fargate launch type.
C. Create an Amazon Simple Queue Sen/ice (Amazon SQS) queue Integrate the queue with an Amazon EventBridge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon EC2 Spot Instances Configure a Spot Fleet with a default allocation strategy.
D. Create an Amazon API Gateway API Integrate the API with anAWS Step Functions state machine to receive payment notifications from mobile devices Invoke the state machine to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Sen/ice (Amazon EKS). Configure an EKS cluster with self-managed nodes.

Answer: B

Explanation:
This option is the best solution because it allows the company to run its payment application on AWS with minimal operational overhead and infrastructure management. By using Amazon API Gateway, the company can create a secure and scalable API to receive payment notifications from mobile devices. By using AWS Lambda, the company can run a serverless function to validate the payment notifications and send them to the backend application. Lambda handles the provisioning, scaling, and security of the function, reducing the operational complexity and cost. By using Amazon ECS with AWS Fargate, the company can run the backend application on a fully managed container service that scales the compute resources automatically and does not require any EC2 instances to manage. Fargate allocates the right amount of CPU and memory for each container and adjusts them as needed.
A: Create an Amazon Simple Queue Service (Amazon SQS) queue Integrate the queue with an Amazon EventBndge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere Create a standalone cluster. This option is not optimal because it requires the company to manage the Kubernetes cluster that runs the backend application. Amazon EKS Anywhere is a deployment option that allows the company to create and operate Kubernetes clusters on-premises or in other environments outside AWS. The company would need to provision, configure, scale, patch, and monitor the cluster nodes, which can increase the operational overhead and complexity. Moreover, the company would need to ensure the connectivity and security between the AWS services and the EKS Anywhere cluster, which can also add challenges and risks.
B: Create an Amazon API Gateway API Integrate the API with anAWS Step Functions state ma-chine to receive payment notifications from mobile devices Invoke the state machine to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Sen/ice (Amazon EKS). Configure an EKS cluster with self-managed nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the Kubernetes cluster that runs the backend application. Amazon EKS is a fully managed service that runs Kubernetes on AWS, but it still requires the company to manage the worker nodes that run the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using AWS Step Functions to validate the payment notifications may be unnecessary and complex, as the validation logic can be implemented in a simpler way with Lambda or other services.
C: Create an Amazon Simple Queue Sen/ice (Amazon SQS) queue Integrate the queue with an Amazon EventBridge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon EC2 Spot Instances Configure a Spot Fleet with a default al-location strategy. This option is not cost-effective because it requires the company to manage the EC2 instances that run the backend application. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using Spot Instances can introduce the risk of interruptions, as Spot Instances are reclaimed by AWS when the demand for On-Demand Instances increases.
The company would need to handle the interruptions gracefully and ensure the availability and reliability of the backend application.
References:
1 Amazon API Gateway - Amazon Web Services
2 AWS Lambda - Amazon Web Services
3 Amazon Elastic Container Service - Amazon Web Services
4 AWS Fargate - Amazon Web Services

NEW QUESTION # 202
[Design Secure Architectures]
A company needs a solution to prevent photos with unwanted content from being uploaded to the company's web application. The solution must not involve training a machine learning (ML) model. Which solution will meet these requirements?

A. Create an Amazon CloudFront function that uses Amazon Comprehend to detect unwanted content. Associate the function with the web application.
B. Create an AWS Lambda function that uses Amazon Rekognition to detect unwanted content. Create a Lambda function URL that the web application invokes when new photos are uploaded.
C. Create and deploy a model by using Amazon SageMaker Autopilot. Create a real-time endpoint that the web application invokes when new photos are uploaded.
D. Create an AWS Lambda function that uses Amazon Rekognition Video to detect unwanted content. Create a Lambda function URL that the web application invokes when new photos are uploaded.

Answer: B

Explanation:
The solution that will meet the requirements is to create an AWS Lambda function that uses Amazon Rekognition to detect unwanted content, and create a Lambda function URL that the web application invokes when new photos are uploaded. This solution does not involve training a machine learning model, as Amazon Rekognition is a fully managed service that provides pre-trained computer vision models for image and video analysis. Amazon Rekognition can detect unwanted content such as explicit or suggestive adult content, violence, weapons, drugs, and more. By using AWS Lambda, the company can create a serverless function that can be triggered by an HTTP request from the web application. The Lambda function can use the Amazon Rekognition API to analyze the uploaded photos and return a response indicating whether they contain unwanted content or not.
The other solutions are not as effective as the first one because they either involve training a machine learning model, do not support image analysis, or do not work with photos. Creating and deploying a model by using Amazon SageMaker Autopilot involves training a machine learning model, which is not required for the scenario. Amazon SageMaker Autopilot is a service that automatically creates, trains, and tunes the best machine learning models for classification or regression based on the data provided by the user. Creating an Amazon CloudFront function that uses Amazon Comprehend to detect unwanted content does not support image analysis, as Amazon Comprehend is a natural language processing service that analyzes text, not images. Amazon Comprehend can extract insights and relationships from text such as language, sentiment, entities, topics, and more. Creating an AWS Lambda function that uses Amazon Rekognition Video todetect unwanted content does not work with photos, as Amazon Rekognition Video is designed for analyzing video streams, not static images. AmazonRekognition Video can detect activities, objects, faces, celebrities, text, and more in video streams.
Reference:
Amazon Rekognition
AWS Lambda
Detecting unsafe content - Amazon Rekognition
Amazon SageMaker Autopilot
Amazon Comprehend

NEW QUESTION # 203
A group requires permissions to list an Amazon S3 bucket and delete objects from that bucket An administrator has created the following 1AM policy to provide access to the bucket and applied that policy to the group. The group is not able to delete objects in the bucket. The company follows least-privilege access rules.

Answer: C

Explanation:
Explanation
{
"Version": "2012-10-17",
"Statement": [
{
"Action": [
"s3:ListBucket",
"s3:DeleteObject"
],
"Resource": [
"arn:aws:s3:::<bucket-name>"
],
"Effect": "Allow",
},
{
"Action": "s3:*DeleteObject",
"Resource": [
"arn:aws:s3:::<bucket-name>/*" # <- The policy clause kludge "added" to match the solution (Q248.1) example
],
"Effect": "Allow"
}
]
}

NEW QUESTION # 204
[Design High-Performing Architectures]
A company stores multiple Amazon Machine Images (AMIs) in an AWS account to launch its Amazon EC2 instances. The AMIs contain critical data and configurations that are necessary for the company's operations. The company wants to implement a solution that will recover accidentally deleted AMIs quickly and efficiently.
Which solution will meet these requirements with the LEAST operational overhead?

A. Copy all AMIs to another AWS account periodically.
B. Create Amazon Elastic Block Store (Amazon EBS) snapshots of the AMIs. Store the snapshots in a separate AWS account.
C. Upload the AMIs to an Amazon S3 bucket that has Cross-Region Replication.
D. Create a retention rule in Recycle Bin.

Answer: D

Explanation:
Recycle Bin is a data recovery feature that enables you to restore accidentally deleted Amazon EBS snapshots and EBS-backed AMIs. When using Recycle Bin, if your resources are deleted, they are retained in the Recycle Bin for a time period that you specify before being permanently deleted. You can restore a resource from the Recycle Bin at any timebefore its retention period expires. This solution has the least operational overhead, as you do not need to create, copy, or upload any additional resources. You can also manage tags and permissions for AMIs in the Recycle Bin. AMIs in the Recycle Bin do not incur any additional charges. Reference:
Recover AMIs from the Recycle Bin
Recover an accidentally deleted Linux AMI

NEW QUESTION # 205
......

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