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Amazon AWS-Solutions-Architect-Professional (AWS Certified Solutions Architect - Professional) Certification Exam is a highly respected certification in the IT industry. It is designed to validate the candidate's expertise in designing and deploying scalable, highly available, and fault-tolerant systems on AWS. AWS Certified Solutions Architect - Professional certification exam covers a wide range of topics and requires candidates to demonstrate their ability to apply their knowledge to real-world scenarios. Holding the AWS Certified Solutions Architect - Professional certification is a valuable asset for professionals who want to advance their career in cloud computing and demonstrate their expertise to employers and clients.

AWS Certified Solutions Architect - Professional certification holders are highly sought after in the industry, as they have demonstrated a mastery of advanced AWS concepts and are capable of designing complex and highly available systems. AWS Certified Solutions Architect - Professional certification can open up numerous career opportunities and can lead to higher salaries and greater job security for those who achieve it.

Amazon AWS-Solutions-Architect-Professional, also known as the AWS Certified Solutions Architect - Professional exam, is a certification program that validates an individual's expertise in designing and deploying scalable, reliable, and secure applications in the Amazon Web Services (AWS) cloud environment. AWS Certified Solutions Architect - Professional certification is intended for experienced cloud architects, solution designers, and IT professionals who are looking to enhance their knowledge and skills in AWS cloud infrastructure.

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Amazon AWS Certified Solutions Architect - Professional Sample Questions (Q411-Q416):

NEW QUESTION # 411
A company is developing several critical long-running applications hosted on Docker.
How should a Solutions Architect design a solution to meet the scalability and orchestration requirements
on AWS?

  • A. Use Amazon ECS and Service Auto Scaling.
  • B. Use Auto Scaling groups to launch containers on existing Amazon EC2 instances.
  • C. Use Spot Instances for orchestration and for scaling containers on existing Amazon EC2 instances.
  • D. Use AWS OpsWorks to launch containers in new Amazon EC2 instances.

Answer: A

Explanation:
Explanation/Reference:
Reference https://aws.amazon.com/getting-started/tutorials/deploy-docker-containers/


NEW QUESTION # 412
A company is developing a new service that will be accessed using TCP on a static port. A solutions architect must ensure that the service is highly available, has redundancy across Availability Zones, and is accessible using the DNS name my.service.com, which is publicly accessible. The service must use fixed address assignments so other companies can add the addresses to their allow lists.
Assuming that resources are deployed in multiple Availability Zones in a single Region, which solution will meet these requirements?

  • A. Create an Amazon ECS cluster and a service definition for the application. Create and assign public IP address for each host in the cluster. Create an Application Load Balancer (ALB) and expose the static TCP port. Create a target group and assign the ECS service definition name to the ALB. Create a new CNAME record set and associate the public IP addresses to the record set. Provide the Elastic IP addresses of the Amazon EC2 instances to the other companies to add to their allow lists.
  • B. Create an Amazon ECS cluster and a service definition for the application. Create and assign public IP addresses for the ECS cluster. Create a Network Load Balancer (NLB) and expose the TCP port. Create a target group and assign the ECS cluster name to the NLB. Create a new A record set named my.service.com, and assign the public IP addresses of the ECS cluster to the record set. Provide the public IP addresses of the ECS cluster to the other companies to add to their allow lists.
  • C. Create Amazon EC2 instances with an Elastic IP address for each instance. Create a Network Load Balancer (NLB) and expose the static TCP port. Register EC2 instances with the NLB. Create a new name server record set named my.service.com, and assign the Elastic IP addresses of the EC2 instances to the record set. Provide the Elastic IP addresses of the EC2 instances to the other companies to add to their allow lists.
  • D. Create Amazon EC2 instances for the service. Create one Elastic IP address for each Availability Zone.
    Create a Network Load Balancer (NLB) and expose the assigned TCP port. Assign the Elastic IP addresses to the NLB for each Availability Zone. Create a target group and register the EC2 instances with the NLB. Create a new A (alias) record set named my.service.com, and assign the NLB DNS name to the record set.

Answer: A


NEW QUESTION # 413
A team of data scientists is using Amazon SageMaker instances and SageMaker APIs to train machine learning (ML) models. The SageMaker instances are deployed in a VPC that does not have access to or from the internet. Datasets for ML model training are stored in an Amazon S3 bucket. Interface VPC endpoints provide access to Amazon S3 and the SageMaker APIs.
Occasionally, the data scientists require access to the Python Package Index (PyPl) repository to update Python packages that they use as part of their workflow. A solutions architect must provide access to the PyPI repository while ensuring that the SageMaker instances remain isolated from the internet.
Which solution will meet these requirements?

  • A. Create a NAT instance in the VPC. Configure VPC routes to allow access to the internet. Configure SageMaker notebook instance firewall rules that allow access to only the PyPI repository endpoint.
  • B. Create an AWS CodeCommit repository for each package that the data scientists need to access.
    Configure code synchronization between the PyPl repository and the CodeCommit repository. Create a VPC endpoint for CodeCommit.
  • C. Create an AWS CodeArtifact domain and repository. Add an external connection for public:pypi to the CodeArtifact repository. Configure the Python client to use the CodeArtifact repository. Create a VPC endpoint for CodeArtifact.
  • D. Create a NAT gateway in the VPC. Configure VPC routes to allow access to the internet with a network ACL that allows access to only the PyPl repository endpoint.

Answer: C


NEW QUESTION # 414
A user is thinking to use EBS PIOPS volume.
Which of the below mentioned options is a right use case for the PIOPS EBS volume?

  • A. Mongo DB
  • B. Log processing
  • C. System boot volume
  • D. Analytics

Answer: A

Explanation:
Explanation
Provisioned IOPS volumes are designed to meet the needs of I/O-intensive workloads, particularly database workloads that are sensitive to storage performance and consistency in random access I/O throughput.
Provisioned IOPS volumes are designed to meet the needs of I/O-intensive workloads, particularly database workloads, that are sensitive to storage performance and consistency in random access I/O throughput business applications, database workloads, such as NoSQL DB, RDBMS, etc.
http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSVolumeTypes.html


NEW QUESTION # 415
A company manufactures smart vehicles. The company uses a custom application to collect vehicle data. The vehicles use the MQTT protocol to connect to the application.
The company processes the data in 5-minute intervals. The company then copies vehicle telematics data to on-premises storage. Custom applications analyze this data to detect anomalies.
The number of vehicles that send data grows constantly. Newer vehicles generate high volumes of data. The on-premises storage solution is not able to scale for peak traffic, which results in data loss. The company must modernize the solution and migrate the solution to AWS to resolve the scaling challenges.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon MQ for RabbitMQ to collect the vehicle data. Send the data to an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Use Amazon Lookout for Metrics to detect anomalies.
  • B. Use AWS IOT FleetWise to collect the vehicle data. Send the data to an Amazon Kinesis data stream.
    Use an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Use the built-in machine learning transforms in AWS Glue to detect anomalies.
  • C. Use AWS IOT Greengrass to send the vehicle data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Create an Apache Kafka application to store the data in Amazon S3. Use a pretrained model in Amazon SageMaker to detect anomalies.
  • D. Use AWS IOT Core to receive the vehicle data. Configure rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3. Create an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies.

Answer: D

Explanation:
Explanation
Using AWS IoT Core to receive the vehicle data will enable connecting the smart vehicles to the cloud using the MQTT protocol1. AWS IoT Core is a platform that enables you to connect devices to AWS Services and other devices, secure data and interactions, process and act upon device data, and enable applications to interact with devices even when they are offline2. Configuring rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3 will enable processing and storing the vehicle data in a scalable and reliable way3. Amazon Kinesis Data Firehose is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3. Creating an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies will enable analyzing the vehicle data using SQL queries or Apache Flink applications. Amazon Kinesis Data Analytics is a fully managed service that enables you to process and analyze streaming data using SQL or Java.


NEW QUESTION # 416
......

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