Pass the Amazon Web Services AWS Certified Professional SAP-C02 Questions and answers with CertsForce

Option B is correct because creating an IAM role in the application account that has permissions to access the secrets and creating an IAM role in the DBA account that has permissions to assume the role in the application account eliminates the need to manually share the secrets. This approach uses cross-account IAM roles to grant access to the secrets in the application account. The database administrators canassume the role in the application account from their EC2 instance in the DBA account and retrieve the secrets without having to store them locally or share them manually2

The best migration strategy to meet the requirement of minimizing database service interruption before the DNS cutover is to use AWS DMS to migrate data between the two Aurora DB clusters. AWS DMS can perform continuous replication of data with high availability and consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift and Amazon S31. AWS DMS supports homogeneous migrations such as migrating from one Aurora MySQL DB cluster to another, as well as heterogeneous migrations between different database platforms2. AWS DMS also supports cross-account migrations, as long as the source and target databases are in the same AWS Region3.

The other options are not optimal for the following reasons:

Option A: Taking a snapshot of the existing Aurora database and restoring it in the new account would require a downtime during the snapshot and restore process, which could be significant for large databases. Moreover, any changes made to the source database after the snapshot would not be replicated to the target database, resulting in data inconsistency4.

Option C: Using AWS Backup to share an Aurora database backup from the existing AWS account to the new AWS account would have the same drawbacks as option A, as AWS Backup uses snapshots to create backups of Aurora databases.

Option D: Using AWS Application Migration Service to migrate data between the two Aurora DB clusters is not a valid option, as AWS Application Migration Service is designed to migrate applications, not databases, to AWS. AWS Application Migration Service can migrate applications from on-premises or other cloud environments to AWS, using agentless or agent-based methods.

(https://docs.aws.amazon.com/controltower/latest/userguide/strongly-recommended-guardrails.html)

 The company should create a resource-based IAM policy that includes conditions for specific DynamoDB attributes (fine-grained access control). The company should attach the policy to the DynamoDB table. In the marketing team’s account, the company should create an IAM role that has permissions to access the DynamoDB table in the finance team’s account. This solution will meet the requirements because a resource-based IAM policy is a policy that you attach to an AWS resource (such as a DynamoDB table) to control who can access that resource and what actions they can perform on it. You can use IAM policyconditions to specify fine-grained access control for DynamoDB items and attributes. For example, you can allow or deny access to specific attributes of all items in a table by matching on attribute names1. By creating a resource-based policy that allows access to only specific attributes of the DynamoDB table and attaching it to the table, the company can restrict access to confidential data. By creating an IAM role in the marketing team’s account that has permissions to access the DynamoDB table in the finance team’s account, the company can enable cross-account access.

The other options are not correct because:

Creating an SCP to grant the marketing team’s AWS account access to the specific attributes of the DynamoDB table would not work because SCPs are policies that you can use with AWS Organizations to manage permissions in your organization’s accounts. SCPs do not grant permissions; instead, they specify the maximum permissions that identities in an account can have2. SCPs cannot be used to specify fine-grained access control for DynamoDB items and attributes.

Creating an IAM role in the finance team’s account by using IAM policy conditions for specific DynamoDB attributes and establishing trust with the marketing team’s account would not work because IAM roles are identities that you can create in your account that have specific permissions. You can use an IAM role to delegate access to users, applications, or services that don’t normally have access to your AWS resources3. However, creating an IAM role in the finance team’s account would not restrict access to specific attributes of the DynamoDB table; it would only allow cross-account access. The company would still need a resource-based policy attached to the table to enforce fine-grained access control.

Creating an IAM role in the finance team’s account to access the DynamoDB table and using an IAM permissions boundary to limit the access to the specific attributes would not work because IAM permissions boundaries are policies that you use to delegate permissions management to other users. You can use permissions boundaries to limit the maximum permissions that an identity-based policy can grant to an IAM entity (user or role)4. Permissions boundaries cannot be used to specify fine-grained access control for DynamoDB items and attributes.

The company needs near-real-time visibility into which EC2 instances are connecting to on-premises databases. The correct telemetry source for network connection metadata at the VPC level is VPC Flow Logs. VPC Flow Logs capture information about IP traffic going to and from network interfaces in a VPC, including source/destination IPs, ports, protocol, and accept/reject decisions. This data can be used to infer which EC2 instance IPs are connecting to database IPs.

The company already uses Splunk on premises, so the solution should deliver these logs to Splunk with minimal delay and operational overhead. Amazon Data Firehose provides a fully managed way to deliver streaming data to supported destinations, including Splunk, with buffering and retry handling. CloudWatch Logs subscription filters can stream log events in near real time from CloudWatch Logs to destinations such as Firehose.

Option B uses the standard pattern: enable VPC Flow Logs to CloudWatch Logs, then create a CloudWatch Logs subscription filter that streams the flow logs to a Firehose delivery stream configured with Splunk as the destination. Because CloudWatch Logs subscription deliveries can batch log events, using a Firehose preprocessing Lambda to extract individual log events is a common approach to format records in a way that Splunk ingests cleanly. This yields near-real-time delivery with low operational overhead.

Option A introduces delay because it exports CloudWatch logs periodically to S3 and requires Splunk to poll S3. It also requires long-lived access keys and periodic batch exports, which is not near real time.

Option C relies on application-level logging changes and batch analytics with Athena, which is not near real time and requires substantial changes and additional pipelines.

Option D is over-engineered for the stated requirement. Using Flink and anomaly detection focuses on anomalies rather than simply identifying connections, and it adds significant operational complexity compared to direct delivery of flow logs to Splunk via Firehose.

Therefore, streaming VPC Flow Logs from CloudWatch Logs to Splunk using a Firehose delivery stream and a subscription filter is the best approach.

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 Exchangefor 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.

Allows client machines to be able to connect to Session Manager using the AWS CLI instead of going through the AWS EC2 or AWS Server Manager console. https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.htmlhttps://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.html#:~:text=aws%20ssm%20start%2Dsession%20%2D%2Dtarget%20instance%2Did

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_org_support-all-features.html

https://docs.aws.amazon.com/singlesignon/latest/userguide/get-started-prereqs-considerations.html

A: Use dynamic IAM policies with {aws:PrincipalTag/userName} to enforceprefix-level access control— i.e., bucket/userA/*, bucket/userB/*.

C: Enable CloudTraildata eventsto capture object-level access andquery them withAthena. This is the AWS-recommended way to audit per-user object access.

Incorrect:

B doesn't provide user isolation.

D only capturesmanagement events, not object-level data access.

E is legacy, inefficient, and not structured for per-user auditing.

This solution meets the requirements by using Application Auto Scaling to automatically increase capacity during the peak period, which will handle the double the average load. And by purchasing reserved RCUs and WCUs to match the average load, it will minimize the cost of the table for the rest of the week when the load is close to the average.

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.htmlhttps://docs.aws.amazon.com/directoryservice/latest/admin-guide/ms_ad_join_instance.html

Amazon S3 Intelligent-Tiering is a storage class that automatically moves objects between two access tiers based on changing access patterns. Objects that are accessed frequently are stored in the frequent access tier and objects that are accessed infrequently are stored in the infrequent access tier. This allows for cost optimization without requiring manual intervention. This makes it an ideal solution for the scenario described, as it can automatically move objects that are infrequently accessed after 30 days to a lower-cost storage tier while still maintaining millisecond retrieval availability.

Option D provisions a new Amazon Connect instance with all existing users and contact flows in a second Region. It also sets up an Amazon Route 53 health check for the URL of the Amazon Connect instance, an Amazon CloudWatch alarm for failed health checks, and an AWS Lambda function to deploy an AWS CloudFormation template that provisions claimed phone numbers. This option allows for the fastest recovery time because all the necessary components are already provisioned and ready to go in the second Region. In the event of a disaster, the failed health check will trigger the AWS Lambda function to deploy the CloudFormation template to provision the claimed phone numbers, which is the only missing component.

The company should create an Amazon CloudFront distribution with the ALB as the origin. The company should add a custom header and random value on the CloudFront domain. The company should configure the ALB to conditionally forward traffic if the header and value match. The company should also deploy an AWS WAF web ACL that includes an appropriate rule group. The company should associate the web ACL with the Amazon CloudFront distribution. This solution will meet the requirements most cost-effectively because Amazon CloudFront is a fast content delivery network (CDN) service that securely deliversdata, videos, applications, and APIs to customers globally with lowlatency, hightransfer speeds, all within a developer-friendly environment1. By creating an Amazon CloudFront distribution with the ALB as the origin, the company can improve the performance andavailability of its application by caching static content at edge locations closer to end users. By adding a custom header and random value on the CloudFront domain, the company can prevent direct access to the ALB and ensure that only requests from CloudFront are forwarded to the ECS tasks. By configuring the ALB to conditionally forward traffic if the header and value match, the company can implement origin access identity (OAI) for its ALB origin. OAI is a feature that enables you to restrict access to your content by requiring users to access your content through CloudFront URLs2. By deploying an AWS WAF web ACL that includes an appropriate rule group, the company can prevent attacks and ensure business continuity with minimal service interruptions during an ongoing attack. AWS WAF is a web application firewall that lets you monitor and control web requests that are forwarded to your web applications. You can use AWS WAF to define customizable web security rules that control which traffic can access your web applications and which traffic should be blocked3. By associating the web ACL with the Amazon CloudFront distribution, the company can apply the web security rules to all requests that are forwarded by CloudFront.

The other options are not correct because:

Deploying the application in two AWS Regions and configuring Amazon Route 53 to route to both Regions with equal weight would not prevent attacks or ensure business continuity. Amazon Route 53 is a highly available and scalable cloud Domain Name System (DNS) web service that routes end users to Internet applications by translating names like www.example.com into numeric IP addresses4. However, routing traffic to multiple Regions would not protect against attacks or provide failover in case of an outage. It would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Configuring auto scaling for Amazon ECS tasks and creating a DynamoDB Accelerator (DAX) cluster would not prevent attacks or ensure business continuity. Auto scaling is a feature that enables you to automatically adjust your ECS tasks based on demand or a schedule. DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement. However, these features would not protect against attacks or provide failover in case of an outage. They would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Configuring Amazon ElastiCache to reduce overhead on DynamoDB would not prevent attacks or ensure business continuity. Amazon ElastiCache is a fully managed in-memory data store service that makes it easy to deploy, operate, and scale popular open-source compatible in-memory data stores. However, this service would not protect against attacks or provide failover in case of an outage. It would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Using AWS Transfer Family to create an FTP server that places the files in Amazon S3 and using S3 event notifications through Amazon Simple Notification Service (Amazon SNS) to invoke an AWS Lambda function will ensure that the archive always receives the files and that the central system is always updated. This solution maximizes scalability and eliminates the need for manual intervention, such as rebooting the EC2 instance.