UTILIZING THE SAP-C02 VALID TEST VCE FREE MEANS THAT YOU HAVE PASSED HALF OF AWS CERTIFIED SOLUTIONS ARCHITECT - PROFESSIONAL (SAP-C02)

Utilizing The SAP-C02 Valid Test Vce Free Means that You Have Passed Half of AWS Certified Solutions Architect - Professional (SAP-C02)

Utilizing The SAP-C02 Valid Test Vce Free Means that You Have Passed Half of AWS Certified Solutions Architect - Professional (SAP-C02)

Blog Article

Tags: SAP-C02 Valid Test Vce Free, Questions SAP-C02 Pdf, Exam SAP-C02 Study Solutions, SAP-C02 Customized Lab Simulation, SAP-C02 Reliable Exam Testking

DOWNLOAD the newest SurePassExams SAP-C02 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1LBTLO3RFfBzT7eLyg_J68Z6WqVCK-oru

As a market leader, our company is able to attract quality staffs on our SAP-C02 exam materials , it actively seeks out those who are energetic, persistent, and professional to various SAP-C02 certificate and good communicator. And we believe that the key of our company's success is its people, skills, and experience on SAP-C02 Study Guide. Over 50% of the account executives and directors have been with the Group for more than ten years. We have strong strenght to lead you to success!

Amazon SAP-C02 (AWS Certified Solutions Architect - Professional (SAP-C02)) certification exam is a highly sought-after certification for professionals seeking a career in cloud computing. SAP-C02 exam is designed to test the candidate's knowledge and expertise in designing and deploying scalable, highly available, and fault-tolerant systems on the Amazon Web Services (AWS) platform.

>> SAP-C02 Valid Test Vce Free <<

Questions SAP-C02 Pdf & Exam SAP-C02 Study Solutions

With the Amazon SAP-C02 certification exam you can do your job nicely and quickly. You should keep in mind that the Amazon SAP-C02 certification exam is a valuable credential and will play an important role in your career advancement. With the right Amazon SAP-C02 Exam Preparation, commitment and dedication you can make this challenge easy and quick.

Amazon SAP-C02 exam is one of the most sought-after certifications for IT professionals who want to validate their skills in designing and deploying scalable and fault-tolerant systems on the Amazon Web Services (AWS) cloud platform. AWS Certified Solutions Architect - Professional (SAP-C02) certification is designed for individuals who have already earned the AWS Certified Solutions Architect – Associate credential and are looking to advance their career by demonstrating their proficiency in complex AWS solutions.

Amazon SAP-C02 certification exam is designed to test the skills and knowledge of professionals who are seeking advanced-level certification as an AWS Solutions Architect. AWS Certified Solutions Architect - Professional (SAP-C02) certification is ideal for those who have already obtained the AWS Certified Solutions Architect - Associate certification and want to take their expertise to the next level. The SAP-C02 Exam is designed to test candidates on their ability to design and deploy highly scalable, fault-tolerant, and secure applications on AWS.

Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q353-Q358):

NEW QUESTION # 353
A company gives users the ability to upload images from a custom application. The upload process invokes an AWS Lambda function that processes and stores the image in an Amazon S3 bucket. The application invokes the Lambda function by using a specific function version ARN.
The Lambda function accepts image processing parameters by using environment variables. The company often adjusts the environment variables of the Lambda function to achieve optimal image processing output. The company tests different parameters and publishes a new function version with the updated environment variables after validating results. This update process also requires frequent changes to the custom application to invoke the new function version ARN. These changes cause interruptions for users.
A solutions architect needs to simplify this process to minimize disruption to users.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create an Amazon DynamoDB table to store the image processing parameters. Modify the Lambda function to retrieve the image processing parameters from the DynamoDB table.
  • B. Create a Lambda function alias. Modify the client application to use the function alias ARN. Reconfigure the Lambda alias to point to new versions of the function when the company finishes testing.
  • C. Directly code the image processing parameters within the Lambda function and remove the environment variables. Publish a new function version when the company updates the parameters.
  • D. Directly modify the environment variables of the published Lambda function version. Use the SLATEST version to test image processing parameters.

Answer: B

Explanation:
A Lambda function alias allows you to point to a specific version of a function and also can be updated to point to a new version of the function without modifying the client application. This way, the company can test different versions of the function with different environment variables and, once the optimal parameters are found, update the alias to point to the new version, without the need to update the client application.
By using this approach, the company can simplify the process of updating the environment variables, minimize disruption to users, and reduce the operational overhead.
Reference:
AWS Lambda documentation: https://aws.amazon.com/lambda/
AWS Lambda Aliases documentation: https://docs.aws.amazon.com/lambda/latest/dg/aliases-intro.html AWS Lambda versioning and aliases documentation: https://aws.amazon.com/blogs/compute/versioning-aliases-in-aws-lambda/


NEW QUESTION # 354
A company has an loT platform that runs in an on-premises environment. The platform consists of a server that connects to loT devices by using the MQTT protocol. The platform collects telemetry data from the devices at least once every 5 minutes The platform also stores device metadata in a MongoDB cluster An application that is installed on an on-premises machine runs periodic jobs to aggregate and transform the telemetry and device metadata The application creates reports that users view by using another web application that runs on the same on-premises machine The periodic jobs take 120-600 seconds to run However, the web application is always running.
The company is moving the platform to AWS and must reduce the operational overhead of the stack.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select THREE.)

  • A. Configure the loT devices to publish to AWS loT Core
  • B. Write the metadata to Amazon DocumentDB (with MongoDB compatibility)
  • C. Use AWS Lambda functions to connect to the loT devices
  • D. Use AWS Step Functions state machines with AWS Lambda tasks to prepare the reports and to write the reports to Amazon S3 Use Amazon CloudFront with an S3 origin to serve the reports
  • E. Use an Amazon Elastic Kubernetes Service (Amazon EKS) cluster with Amazon EC2 instances to prepare the reports Use an ingress controller in the EKS cluster to serve the reports
  • F. Write the metadata to a self-managed MongoDB database on an Amazon EC2 instance

Answer: A,B,D

Explanation:
Explanation
https://aws.amazon.com/step-functions/use-cases/


NEW QUESTION # 355
A company has applications in an AWS account that is named Source. The account is in an organization in AWS Organizations. One of the applications uses AWS Lambda functions and stores inventory data in an Amazon Aurora database. The application deploys the Lambda functions by using a deployment package. The company has configured automated backups for Aurora.
The company wants to migrate the Lambda functions and the Aurora database to a new AWS account that is named Target. The application processes critical data, so the company must minimize downtime.
Which solution will meet these requirements?

  • A. Use AWS Resource Access Manager (AWS RAM) to share the Lambda functions with the Target account. Share the automated Aurora DB cluster snapshot with the Target account.
  • B. Use AWS Resource Access Manager (AWS RAM) to share the Lambda functions and the Aurora DB cluster with the Target account. Grant the Target account permission to clone the Aurora DB cluster.
  • C. Download the Lambda function deployment package from the Source account. Use the deployment package and create new Lambda functions in the Target account. Share the automated Aurora DB cluster snapshot with the Target account.
  • D. Download the Lambda function deployment package from the Source account. Use the deployment package and create new Lambda functions in the Target account Share the Aurora DB cluster with the Target account by using AWS Resource Access Manager {AWS RAM). Grant the Target account permission to clone the Aurora DB cluster.

Answer: D

Explanation:
This solution uses a combination of AWS Resource Access Manager (RAM) and automated backups to migrate the Lambda functions and the Aurora database to the Target account while minimizing downtime. In this solution, the Lambda function deployment package is downloaded from the Source account and used to create new Lambda functions in the Target account. The Aurora DB cluster is shared with the Target account using AWS RAM and the Target account is granted permission to clone the Aurora DB cluster, allowing for a new copy of the Aurora database to be created in the Target account. This approach allows for the data to be migrated to the Target account while minimizing downtime, as the Target account can use the cloned Aurora database while the original Aurora database continues to be used in the Source account.


NEW QUESTION # 356
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. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: B

Explanation:
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 cameras 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 # 357
An auction website enables users to bid on collectible items The auction rules require that each bid is processed only once and in the order it was received The current implementation is based on a fleet of Amazon EC2 web servers that write bid records into Amazon Kinesis Data Streams A single 12 large instance has a cron job that runs the bid processor, which reads incoming bids from Kinesis Data Streams and processes each bid The auction site is growing in popularity, but users are complaining that some bids are not registering Troubleshooting indicates that the bid processor is too slow during peak demand hours sometimes crashes while processing and occasionally loses track of which record is being processed What changes should make the bid processing more reliable?

  • A. Refactor the web application to post each incoming bid to an Amazon SQS FIFO queue in place of Kinesis Data Streams Refactor the bid processor to continuously consume the SQS queue Place the bid processing EC2 instance in an Auto Scaling group with a minimum and a maximum size of 1
  • B. Refactor the web application to post each incoming bid to an Amazon SNS topic in place of Kinesis Data Streams Configure the SNS topic to trigger an AWS Lambda function that B. processes each bid as soon as a user submits it
  • C. Refactor the web application to use the Amazon Kinesis Producer Library (KPL) when posting bids to Kinesis Data Streams Refactor the bid processor to flag each record in Kinesis Data Streams as being unread processing and processed At the start of each bid processing run; scan Kinesis Data Streams for unprocessed records
  • D. Switch the EC2 instance type from t2 large to a larger general compute instance type Put the bid processor EC2 instances in an Auto Scaling group that scales out the number of EC2 instances running the bid processor based on the incomingRecords metric in Kinesis Data Streams

Answer: A

Explanation:
Explanation
https://aws.amazon.com/sqs/faqs/#:~:text=A%20single%20Amazon%20SQS%20message,20%2C000%20for%2


NEW QUESTION # 358
......

Questions SAP-C02 Pdf: https://www.surepassexams.com/SAP-C02-exam-bootcamp.html

P.S. Free 2025 Amazon SAP-C02 dumps are available on Google Drive shared by SurePassExams: https://drive.google.com/open?id=1LBTLO3RFfBzT7eLyg_J68Z6WqVCK-oru

Report this page