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SAP-C02資格認定試験 & SAP-C02対応問題集
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SAP-C02試験の準備方法|最高のSAP-C02資格認定試験試験|完璧なAWS Certified Solutions Architect - Professional (SAP-C02)対応問題集
人生は勝ち負けじゃない、負けたって言わない人が勝ちなのよ。近年Amazon SAP-C02認定試験の難度で大方の受験生は試験に合格しなかったのに面して、勇者のようにこのチャレンジをやってますか。それで、我々社のAmazon SAP-C02無料の試験問題集サンプルを参考します。自分の相応しい復習問題集バージョン(PDF版、ソフト版を、オンライン版)を選んで、ただ学習教材を勉強し、正確の答えを覚えるだけ、Amazon SAP-C02資格認定試験に一度で合格できます。
Amazon AWS Certified Solutions Architect - Professional (SAP-C02) 認定 SAP-C02 試験問題 (Q21-Q26):
質問 # 21
A software-as-a-service (SaaS) provider exposes APIs through an Application Load Balancer (ALB). The ALB connects to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that is deployed in the us-east-1 Region. The exposed APIs contain usage of a few non-standard REST methods: LINK, UNLINK, LOCK, and UNLOCK.
Users outside the United States are reporting long and inconsistent response times for these APIs. A solutions architect needs to resolve this problem with a solution that minimizes operational overhead.
Which solution meets these requirements?
- A. Deploy the APIs to two additional AWS Regions: eu-west-1 and ap-southeast-2. Add latency- based routing records in Amazon Route 53.
- B. Add an Amazon API Gateway edge-optimized API endpoint to expose the APIs. Configure the ALB as the target.
- C. Add an accelerator in AWS Global Accelerator. Configure the ALB as the origin.
- D. Add an Amazon CloudFront distribution. Configure the ALB as the origin.
正解:C
解説:
AWS Global Accelerator is a service that improves the availability and performance of applications for global users. By adding an accelerator in AWS Global Accelerator and configuring the ALB as the origin, the traffic from users outside the United States will be routed through the Global Accelerator network, which uses the AWS global network infrastructure to optimize the delivery of the application traffic.
質問 # 22
A company needs to migrate its customer transactions database from on premises to AWS. The database resides on an Oracle DB instance that runs on a Linux server. According to a new security requirement, the company must rotate the database password each year.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Convert the database to Amazon DynamoDB by using the AWS Schema Conversion Tool (AWS SCT).
Store the password in AWS Systems Manager Parameter Store. Create an Amazon CloudWatch alarm to invoke an AWS Lambda function for yearly password rotation. - B. Migrate the database to Amazon Neptune by using the AWS Schema Conversion Tool {AWS SCT).
Create an Amazon CloudWatch alarm to invoke an AWS Lambda function for yearly password rotation. - C. Migrate the database to an Amazon EC2 instance. Use AWS Systems Manager Parameter Store to keep and rotate the connection string by using an AWS Lambda function on a yearly schedule
- D. Migrate the database to Amazon RDS for Oracle. Store the password in AWS Secrets Manager. Turn on automatic rotation. Configure a yearly rotation schedule.
正解:D
質問 # 23
A company has millions of objects in an Amazon S3 bucket. The objects are in the S3 Standard storage class.
All the S3 objects are accessed frequently. The number of users and applications that access the objects is increasing rapidly. The objects are encrypted with server-side encryption with AWS KMS Keys (SSE-KMS).
A solutions architect reviews the company's monthly AWS invoice and notices that AWS KMS costs are increasing because of the high number of requests from Amazon S3. The solutions architect needs to optimize costs with minimal changes to the application.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS CloudHSM to store the encryption keys. Create a new S3 bucket. Use S3 Batch Operations to copy the existing objects to the new S3 bucket. Encrypt the objects by using the keys from CloudHSM.
- B. Create a new S3 bucket that has server-side encryption with Amazon S3 managed keys (SSE-S3) as the encryption type. Use S3 Batch Operations to copy the existing objects to the new S3 bucket. Specify SSE-S3.
- C. Create a new S3 bucket that has server-side encryption with customer-provided keys (SSE-C) as the encryption type. Copy the existing objects to the new S3 bucket. Specify SSE-C.
- D. Use the S3 Intelligent-Tiering storage class for the S3 bucket. Create an S3 Intelligent-Tiering archive configuration to transition objects that are not accessed for 90 days to S3 Glacier Deep Archive.
正解:B
解説:
Explanation
To reduce the volume of Amazon S3 calls to AWS KMS, use Amazon S3 bucket keys, which are protected encryption keys that are reused for a limited time in Amazon S3. Bucket keys can reduce costs for AWS KMS requests by up to 99%. You can configure a bucket key for all objects in an Amazon S3 bucket, or for a specific object in an Amazon S3 bucket.
https://docs.aws.amazon.com/fr_fr/kms/latest/developerguide/services-s3.html
質問 # 24
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. 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.
- 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. 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.
正解:B
解説:
AWS IoT Greengrass enables ML inference locally using models that are created, trained, and optimized in the cloud using Amazon SageMaker, AWS Deep Learning AMI, or AWS Deep Learning Containers, and deployed on the edge devices.
https://aws.amazon.com/blogs/machine-learning/anomaly-detection-with-amazon-sagemaker- edge-manager-using-aws-iot-greengrass-v2/
質問 # 25
A company wants to migrate its data analytics environment from on premises to AWS The environment consists of two simple Node js applications One of the applications collects sensor data and loads it into a MySQL database The other application aggregates the data into reports When the aggregation jobs run. some of the load jobs fail to run correctly The company must resolve the data loading issue The company also needs the migration to occur without interruptions or changes for the company's customers What should a solutions architect do to meet these requirements'?
- A. Set up an Amazon Aurora MySQL database as a replication target for the on-premises database Create an Aurora Replica for the Aurora MySQL database, and move the aggregation jobs to run against the Aurora Replica Set up collection endpomts as AWS Lambda functions behind a Network Load Balancer (NLB). and use Amazon RDS Proxy to wnte to the Aurora MySQL database When the databases are synced disable the replication job and restart the Aurora Replica as the primary instance. Point the collector DNS record to the NLB.
- B. Set up an Amazon Aurora MySQL database Create an Aurora Replica for the Aurora MySQL database and move the aggregation jobs to run against the Aurora Replica Set up collection endpoints as an Amazon Kinesis data stream Use Amazon Kinesis Data Firehose to replicate the data to the Aurora MySQL database When the databases are synced disable the replication job and restart the Aurora Replica as the primary instance Point the collector DNS record to the Kinesis data stream.
- C. Set up an Amazon Aurora MySQL database Use AWS Database Migration Service (AWS DMS) to perform continuous data replication from the on-premises database to Aurora Move the aggregation jobs to run against the Aurora MySQL database Set up collection endpomts behind an Application Load Balancer (ALB) as Amazon EC2 instances in an Auto Scaling group When the databases are synced, point the collector DNS record to the ALB Disable the AWS DMS sync task after the cutover from on premises to AWS
- D. Set up an Amazon Aurora MySQL database Use AWS Database Migration Service (AWS DMS) to perform continuous data replication from the on-premises database to Aurora Create an Aurora Replica for the Aurora MySQL database and move the aggregation jobs to run against the Aurora Replica Set up collection endpoints as AWS Lambda functions behind an Application Load Balancer (ALB) and use Amazon RDS Proxy to write to the Aurora MySQL database When the databases are synced, point the collector DNS record to the ALB Disable the AWS DMS sync task after the cutover from on premises to AWS
正解:D
質問 # 26
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