Practice Exam AWS Certified Solutions Architect Professional

Practice Exam AWS Certified Solutions Architect Professional

Prepare for your SAP-C01 exam. 75 high-quality practice test questions written from scratch with detailed explanations!

上次更新 1/2021

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Ace your AWS Certified Solutions Architect Professional SAP-C01 exam

Practice with high quality practice exams alongside detailed explanation to learn concepts

The SAP-C01 exams practice exams have been written from scratch

Perfect companion to the "Ultimate AWS Certified Solutions Architect Professional" course by Stephane Maarek

要求

Recommended: Preparing for the exam with "Ultimate AWS Certified Solutions Architect Professional" course by Stephane Maarek

说明

Preparing for AWS Certified Solutions Architect Associate SAP-C01? This is THE practice exams course to give you the winning edge.



These practice exams have been co-authored by Stephane Maarek and Abhishek Singh who bring their collective experience of passing 18 AWS Certifications to the table.



The tone and tenor of the questions mimic the real exam. Along with the detailed description and “exam alert” provided within the explanations, we have also extensively referenced AWS documentation to get you up to speed on all domain areas being tested for the SAP-C01 exam.







We want you to think of this course as the final pit-stop so that you can cross the winning line with absolute confidence and get AWS Certified! Trust our process, you are in good hands.



All questions have been written from scratch! And more questions are being added over time!







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Quality speaks for itself...



SAMPLE QUESTION:



A silicon valley based unicorn startup recently launched a video-sharing social networking service called KitKot. The startup uses AWS Cloud to manage the IT infrastructure. Users upload video files up to 1 GB in size to a single EC2 instance based application server which stores them on a shared EFS file system. Another set of EC2 instances managed via an Auto Scaling group, periodically scans the EFS share directory for new files to process and generate new videos (for thumbnails and composite visual effects) according to the video processing instructions that are uploaded alongside the raw video files. Post-processing, the raw video files are deleted from the EFS file system and the results are stored in an S3 bucket. Links to the processed video files are sent via in-app notifications to the users. The startup has recently found that even as more instances are added to the Auto Scaling Group, many files are processed twice, therefore image processing speed is not improved.



As an AWS Certified Solutions Architect Professional, what would you recommend to improve the reliability of the solution as well as eliminate the redundant processing of video files?



Refactor the application to run from Amazon S3 instead of the EFS file system and upload the video files directly to an S3 bucket via an API Gateway based REST API. Configure an S3 trigger to invoke a Lambda function each time a file is uploaded and the Lambda in turn processes the video and stores the processed files in another bucket. Leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files



Refactor the application to run from S3 instead of EFS and upload the video files directly to an S3 bucket. Configure an S3 trigger to invoke a Lambda function on each video file upload to S3 that puts a message in an SQS queue containing the link and the video processing instructions. Change the video processing application to read from the SQS queue and the S3 bucket. Configure the queue depth metric to scale the size of the Auto Scaling group for video processing instances. Leverage CloudWatch Events to trigger an SNS notification to the user containing the links to the processed files



Refactor the application to run from S3 instead of EFS and upload the video files directly to an S3 bucket. Set CloudWatch Events to trigger a Lambda function on each file upload that puts a message in an SQS queue containing the link and the video processing instructions. Change the video processing application to read from SQS queue for new files and configure the queue depth metric to scale instances in the video processing Auto Scaling group. Leverage CloudWatch Events to trigger an SNS notification to the user containing the links to the processed files



Create an hourly cron job on the application server to synchronize the contents of the EFS share with S3. Trigger a Lambda function every time a file is uploaded to S3 and process the video file to store the results in another S3 bucket. Once the file is processed, leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files







What's your guess? Scroll below for the answer...



















































































Correct: 2.



Refactor the application to run from S3 instead of EFS and upload the video files directly to an S3 bucket. Configure an S3 trigger to invoke a Lambda function on each video file upload to S3 that puts a message in an SQS queue containing the link and the video processing instructions. Change the video processing application to read from the SQS queue and the S3 bucket. Configure the queue depth metric to scale the size of the Auto Scaling group for video processing instances. Leverage CloudWatch Events to trigger an SNS notification to the user containing the links to the processed files







For the given use-case, the primary way to address the issues related to reliability, as well as redundant processing of video files, is by introducing SQS into the solution stack. SQS offers a secure, durable, and available hosted queue that lets you integrate and decouple distributed software systems and components. SQS locks your messages during processing, so that multiple producers can send and multiple consumers can receive messages at the same time. Using the right combination of delay queues and visibility timeout, you can optimize the solution to address use-cases where the consumer application needs additional time to process messages such as the one in this scenario. Messages are put into the SQS queue via a Lambda function that is triggered when a new video file is uploaded to S3 for processing.







To ensure that the consumer applications running on the video processing instances can scale via an Auto Scaling group, you could use the SQS queue depth (known as the CloudWatch Amazon SQS metric - ApproximateNumberOfMessages) as the underlying metric. However, the issue with using a CloudWatch Amazon SQS metric like ApproximateNumberOfMessagesVisible for target tracking is that the number of messages in the queue might not change proportionally to the size of the Auto Scaling group that processes messages from the queue. An optimized solution would be to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain.







Incorrect options:







Refactor the application to run from Amazon S3 instead of the EFS file system and upload the video files directly to an S3 bucket via an API Gateway based REST API. Configure an S3 trigger to invoke a Lambda function each time a file is uploaded and the Lambda, in turn, processes the video and stores the processed files in another bucket. Leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files - API Gateway supports payload size of only up to 10 MB therefore this option is incorrect for the given use-case since you need to support file sizes of up to 1GB for video processing.







Refactor the application to run from S3 instead of EFS and upload the video files directly to an S3 bucket. Set CloudWatch Events to trigger a Lambda function on each file upload that puts a message in an SQS queue containing the link and the video processing instructions. Change the video processing application to read from SQS queue for new files and configure the queue depth metric to scale instances in the video processing Auto Scaling group. Leverage CloudWatch Events to trigger an SNS notification to the user containing the links to the processed files - CloudWatch Events cannot trigger a Lambda function upon a new file upload in S3 as this event pattern is not supported.



S3 supports only the following destinations where it can publish events - SNS, SQS and Lambda. Therefore this option is incorrect.



Create an hourly cron job on the application server to synchronize the contents of the EFS share with S3. Trigger a Lambda function every time a file is uploaded to S3 and process the video file to store the results in another S3 bucket. Once the file is processed, leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files - The issue with this option is lack of reliability. In case the Lambda function (which is triggered when a video file is uploaded to S3) fails to process a given video file, then the source video file would always remain unprocessed as there is no queue-based mechanism to re-process failed events. So this option is incorrect.







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Welcome to the best practice exams to help you prepare for your AWS Certified Solutions Architect Professional exam.



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Anyone preparing for the AWS Certified Solutions Architect Associate SAP-C01