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Amazon SAP-C02 Certification Exam is a valuable credential for professionals who specialize in cloud computing and solutions architecture. SAP-C02 exam tests the candidate's knowledge and skills in various domains related to AWS services and architecture principles. Successfully passing the exam can open up many career opportunities and demonstrate one's ability to design and deploy scalable and highly available systems on AWS.
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The AWS Certified Solutions Architect - Professional (SAP-C02) certification exam consists of 75 multiple-choice and multiple-response questions and has a duration of 180 minutes. The passing score for the exam is 750 out of 1000. Candidates who pass the exam are awarded the AWS Certified Solutions Architect - Professional certification, which is valid for three years. AWS Certified Solutions Architect - Professional (SAP-C02) certification can be renewed by passing the current version of the exam or a higher-level certification exam.
NEW QUESTION # 489
A company runs an application on AWS. The company curates data from several different sources. The company uses proprietary algorithms to perform data transformations and aggregations. After the company performs E TL processes, the company stores the results in Amazon Redshift tables. The company sells this data to other companies. The company downloads the data as files from the Amazon Redshift tables and transmits the files to several data customers by using FTP. The number of data customers has grown significantly. Management of the data customers has become difficult.
The company will use AWS Data Exchange to create a data product that the company can use to share data with customers. The company wants to confirm the identities of the customers before the company shares dat a. The customers also need access to the most recent data when the company publishes the data.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The company should download the data from the Amazon Redshift tables to an Amazon S3 bucket periodically and use AWS Data Exchange for S3 to share data with customers. The company should configure subscription verification and require the data customers to subscribe to the data product. This solution will meet the requirements with the least operational overhead because AWS Data Exchange for S3 is a feature that enables data subscribers to access third-party data files directly from data providers' Amazon S3 buckets. Subscribers can easily use these files for their data analysis with AWS services without needing to create or manage data copies. Data providers can easily set up AWS Data Exchange for S3 on top of their existing S3 buckets to share direct access to an entire S3 bucket or specific prefixes and S3 objects. AWS Data Exchange automatically manages subscriptions, entitlements, billing, and payment1.
The other options are not correct because:
Using AWS Data Exchange for APIs to share data with customers would not work because AWS Data Exchange for APIs is a feature that enables data subscribers to access third-party APIs directly from data providers' AWS accounts. Subscribers can easily use these APIs for their data analysis with AWS services without needing to manage API keys or tokens. Data providers can easily set up AWS Data Exchange for APIs on top of their existing API Gateway resources to share direct access to an entire API or specific routes and stages2. However, this feature is not suitable for sharing data from Amazon Redshift tables, which are not exposed as APIs.
Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not work because the Data API is a feature that enables you to query your Amazon Redshift cluster using HTTP requests, without needing a persistent connection or a SQL client3. It is useful for building applications that interact with Amazon Redshift, but not for sharing data files with customers.
Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not work because AWS Data Exchange does not support datashares for Amazon Redshift clusters. A datashare is a feature that enables you to share live and secure access to your Amazon Redshift data across your accounts or with third parties without copying or moving the underlying data4. It is useful for sharing query results and views with other users, but not for sharing data files with customers.
Publishing the Amazon Redshift data to an Open Data on AWS Data Exchange would not work because Open Data on AWS Data Exchange is a feature that enables you to find and use free and public datasets from AWS customers and partners. It is useful for accessing open and free data, but not for confirming the identities of the customers or charging them for the data.
Reference:
https://aws.amazon.com/data-exchange/why-aws-data-exchange/s3/
https://aws.amazon.com/data-exchange/why-aws-data-exchange/api/
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html
https://docs.aws.amazon.com/redshift/latest/dg/datashare-overview.html
https://aws.amazon.com/data-exchange/open-data/
NEW QUESTION # 490
Question:
A solutions architect is importing a VM from an on-premises environment by using the Amazon EC2 VM Import feature. The imported instance has a public IP and runs in a public subnet in a VPC. However, the instance doesnot appearin the AWS Systems Manager (SSM) console as a managed instance.
Which combination of steps should the architect take to resolve the issue? (Select TWO.)
Answer: B,C
Explanation:
A is correct becauseSSM Agent must be installed and actively runningon the instance for it to be managed by Systems Manager. Imported VMs typically do not have the agent installed by default.
B is also correct becausean IAM instance profilewith at least the AmazonSSMManagedInstanceCore policy must be attached to the instance to allow communication with SSM.
* Option C is unnecessary if the instance has internet access (via public IP).
* Option D relates to migration assessments, not Systems Manager.
* Option E is good practice but not required for the instance to appear as managed.
References:
Managed Instance Requirements
SSM Agent Installation
NEW QUESTION # 491
A global ecommerce company has many data centers worldwide. The company needs scalable cloud storage for legacy file applications. Requirements:
* Must support iSCSI access from on-premises servers.
* Must support point-in-time snapshots via AWS Backup.
* Must retain low-latency access to frequently accessed data.Which solution will meet these requirements?
Answer: B
Explanation:
C is correct because the Storage Gateway Volume Gateway in cache mode supportsiSCSI block storage, which is exactly what legacy applications need. It also caches frequently accessed data locally for low-latency performance and integrates with AWS Backup for point-in-time copies.
A is incorrect - tape gateway is designed for archival backup only.
B and D are incorrect because FSx and file gateway offerfile(NFS/SMB) storage, not block/iSCSI support.
References:
Volume Gateway Overview
AWS Backup Integration
NEW QUESTION # 492
A solutions architect is evaluating the reliability of a recently migrated application running on AWS. The front end is hosted on Amazon S3 and accelerated by Amazon CloudFront. The application layer is running in a stateless Docker container on an Amazon EC2 On-Demand Instance with an Elastic IP address. The storage layer is a MongoDB database running on an EC2 Reserved Instance in the same Availability Zone as the application layer.
Which combination of steps should the solutions architect take to eliminate single points of failure with minimal application code changes? (Choose two.)
Answer: A,E
Explanation:
https://aws.amazon.com/documentdb/?nc1=h_ls
https://aws.amazon.com/blogs/containers/using-alb-ingress-controller-with-amazon-eks-on- fargate/
NEW QUESTION # 493
A company that provisions job boards for a seasonal workforce is seeing an increase in traffic and usage. The backend services run on a pair of Amazon EC2 instances behind an Application Load Balancer with Amazon DynamoDB as the datastore. Application read and write traffic is slow during peak seasons.
Which option provides a scalable application architecture to handle peak seasons with the LEAST development effort?
Answer: A
Explanation:
Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.
Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.
Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.
Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.
References:
Auto Scaling groups
DynamoDB auto scaling
AWS Lambda
DynamoDB global tables
AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform
Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB AWS Aurora Global Database vs. DynamoDB Global Tables Amazon Simple Queue Service (SQS)
NEW QUESTION # 494
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