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    The AWS Certified Solutions Architect - Professional (SAP-C02) exam covers a wide range of topics, including AWS services and their integration, AWS architecture best practices, disaster recovery and business continuity, migration strategies, and security best practices. SAP-C02 exam also tests the candidate's knowledge of AWS cost optimization, deployment and management, and troubleshooting.

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    Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q283-Q288):

    NEW QUESTION # 283
    A company is running a two-tier web-based application in an on-premises data center. The application layer consists of a single server running a stateful application. The application connects to a PostgreSQL database running on a separate server. The application's user base is expected to grow significantly, so the company is migrating the application and database to AWS. The solution will use Amazon Aurora PostgreSQL. Amazon EC2 Auto Scaling, and Elastic Load Balancing.
    Which solution will provide a consistent user experience that will allow the application and database tiers to scale?

    • A. Enable Aurora Auto Scaling for Aurora writers. Use an Application Load Balancer with the round robin routing algorithm and sticky sessions enabled
    • B. Aurora Auto Scaling for Aurora writers. Use a Network Load Balancer with the least outstanding requests routing algorithm and sticky sessions enabled.
    • C. Enable Aurora Auto Scaling for Aurora Replicas. Use a Network Load Balancer with the least outstanding requests routing algorithm and sticky sessions enabled
    • D. Aurora Auto Scaling for Aurora Replicas. Use an Application Load Balancer with the round robin routing algorithm and sticky sessions enabled.

    Answer: D

     

    NEW QUESTION # 284
    A company has an on-premises monitoring solution using a PostgreSQL database for persistence of events. The database is unable to scale due to heavy ingestion and it frequently runs out of storage.
    The company wants to create a hybrid solution and has already set up a VPN connection between its network and AWS.
    The solution should include the following attributes:
    * Managed AWS services to minimize operational complexity
    * A buffer that automatically scales to match the throughput of data and requires no on-going administration.
    * A visualization toot to create dashboards to observe events in near-real time.
    * Support for semi -structured JSON data and dynamic schemas.
    Which combination of components will enabled company to create a monitoring solution that will satisfy these requirements? (Select TWO)

    • A. Configure an Amazon Neptune 0 DB instance to receive events Use Amazon QuickSight to read from the database and create near-real-time visualizations and dashboards
    • B. Configure an Amazon Aurora PostgreSQL DB cluster to receive events Use Amazon Quick Sight to read from the database and create near-real-time visualizations and dashboards
    • C. Create an Amazon Kinesis data stream to buffer events Create an AWS Lambda function to process and transform evens
    • D. Use Amazon Kinesis Data Firehose to buffer events Create an AWS Lambda function 10 process and transform events
    • E. Configure Amazon Elasticsearch Service (Amazon ES) to receive events Use the Kibana endpoint deployed with Amazon ES to create near-real-time visualizations and dashboards.

    Answer: A,E

     

    NEW QUESTION # 285
    A manufacturing company is building an inspection solution for its factory. The company has IP cameras at the end of each assembly line. The company has used Amazon SageMaker to train a machine learning (ML) model to identify common defects from still images.
    The company wants to provide local feedback to factory workers when a defect is detected. The company must be able to provide this feedback even if the factory's internet connectivity is down. The company has a local Linux server that hosts an API that provides local feedback to the workers.
    How should the company deploy the ML model to meet these requirements?

    • A. Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.
    • B. Order an AWS Snowball device. Deploy a SageMaker endpoint the ML model and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected.
    • C. Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model. Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected.
    • D. Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected.

    Answer: A

    Explanation:
    The company should use AWS IoT Greengrass to deploy the ML model to the local server and provide local feedback to the factory workers. AWS IoT Greengrass is a service that extends AWS cloud capabilities to local devices, allowing them to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks1. AWS IoT Greengrass also supports ML inference at the edge, enabling devices to run ML models locally without requiring internet connectivity2.
    The other options are not correct because:
    Setting up an Amazon Kinesis video stream from each IP camera to AWS would not work if the factory's internet connectivity is down. It would also incur unnecessary costs and latency to stream video data to the cloud and back.
    Ordering an AWS Snowball device would not be a scalable or cost-effective solution for deploying the ML model. AWS Snowball is a service that provides physical devices for data transfer and edge computing, but it is not designed for continuous operation or frequent updates3.
    Deploying Amazon Monitron devices on each IP camera would not work because Amazon Monitron is a service that monitors the condition and performance of industrial equipment using sensors and machine learning, not cameras4.
    References:
    https://aws.amazon.com/greengrass/
    https://docs.aws.amazon.com/greengrass/v2/developerguide/use-machine-learning-inference.html
    https://aws.amazon.com/snowball/
    https://aws.amazon.com/monitron/

     

    NEW QUESTION # 286
    A company runs an loT application in the AWS Cloud. The company has millions of sensors that collect data from houses in the United States. The sensors use the MOTT protocol to connect and send data to a custom MQTT broker. The MQTT broker stores the data on a single Amazon EC2 instance. The sensors connect to the broker through the domain named iot.example.com. The company uses Amazon Route 53 as its DNS service. The company stores the data in Amazon DynamoDB.
    On several occasions, the amount of data has overloaded the MOTT broker and has resulted in lost sensor dat a. The company must improve the reliability of the solution.
    Which solution will meet these requirements?

    • A. Set up AWS loT Core to receive the sensor data. Create and configure a custom domain to connect to AWS loT Core. Update the DNS record in Route 53 to point to the AWS loT Core Data-ATS endpoint. Configure an AWS loT rule to store the data.
    • B. Set up AWS loT Greengrass to receive the sensor data. Update the DNS record in Route 53 to point to the AWS loT Greengrass endpoint. Configure an AWS loT rule to invoke an AWS Lambda function to store the data.
    • C. Create a Network Load Balancer (NLB). Set the MQTT broker as the target. Create an AWS Global Accelerator accelerator. Set the NLB as the endpoint for the accelerator. Update the DNS record in Route 53 to a multivalue answer record. Set the Global Accelerator IP addresses as values. Use the MQTT broker to store the data.
    • D. Create an Application Load Balancer (ALB) and an Auto Scaling group for the MOTT broker. Use the Auto Scaling group as the target for the ALB. Update the DNS record in Route 53 to an alias record. Point the alias record to the ALB. Use the MQTT broker to store the data.

    Answer: D

    Explanation:
    it describes a solution that uses an Application Load Balancer (ALB) and an Auto Scaling group for the MQTT broker. The ALB distributes incoming traffic across the instances in the Auto Scaling group and allows for automatic scaling based on incoming traffic. The use of an alias record in Route 53 allows for easy updates to the DNS record without changing the IP address. This solution improves the reliability of the MQTT broker by allowing it to automatically scale based on incoming traffic, reducing the likelihood of lost data due to broker overload.
    Reference:
    https://aws.amazon.com/elasticloadbalancing/applicationloadbalancer/
    https://aws.amazon.com/autoscaling/
    https://aws.amazon.com/route53/

     

    NEW QUESTION # 287
    A company is running several workloads in a single AWS account. A new company policy states that engineers can provision only approved resources and that engineers must use AWS CloudFormation to provision these resources. A solutions architect needs to create a solution to enforce the new restriction on the IAM role that the engineers use for access.
    What should the solutions architect do to create the solution?

    • A. Upload AWS CloudFormation templates that contain approved resources to an Amazon S3 bucket. Update the IAM policy for the engineers' IAM role to only allow access to Amazon S3 and AWS CloudFormation. Use AWS CloudFormation templates to provision resources.
    • B. Update the IAM policy for the engineers' IAM role with permissions to only allow AWS CloudFormation actions. Create a new IAM policy with permission to provision approved resources, and assign the policy to a new IAM service role. Assign the IAM service role to AWS CloudFormation during stack creation.
    • C. Provision resources in AWS CloudFormation stacks. Update the IAM policy for the engineers' IAM role to only allow access to their own AWS CloudFormation stack.
    • D. Update the IAM policy for the engineers' IAM role with permissions to only allow provisioning of approved resources and AWS CloudFormation. Use AWS CloudFormation templates to create stacks with approved resources.

    Answer: D

    Explanation:
    To enforce the new restriction on the IAM role that the engineers use for access, the solutions architect should update the IAM policy for the engineers' IAM role with permissions to only allow provisioning of approved resources and AWS CloudFormation. By doing this, the engineers will only be able to create resources that are explicitly approved and will be forced to use AWS CloudFormation to provision these resources.
    The solutions architect should also use AWS CloudFormation templates to create stacks with approved resources. This allows the engineers to provision resources using the approved templates and ensures that only approved resources are created.
    Reference:
    https://aws.amazon.com/iam/
    https://aws.amazon.com/cloudformation/

     

    NEW QUESTION # 288
    ......

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