SAP-C02 Exam Dumps - Amazon Web Services AWS Certified Professional Questions and Answers

The goal is to ensure the application operates across multiple Availability Zones. That means removing single points of failure in compute, database, and network egress for private subnets.

For compute, the Auto Scaling group currently has min=1 and max=1, which guarantees only one instance is running at a time. Even if the Auto Scaling group spans multiple subnets, a single-instance configuration cannot provide multi-AZ active capacity because a failure of the Availability Zone hosting the single instance would cause downtime until a new instance is launched in a different zone. To operate across multiple Availability Zones, the solution should run multiple instances across different AZs, which implies increasing the minimum capacity above 1 and allowing the group to launch across multiple subnets/AZs.

For the database, a single-AZ RDS for MySQL instance is a single point of failure. Converting to an RDS Multi-AZ configuration provides synchronous replication to a standby in a different Availability Zone and managed failover to maintain availability when the primary AZ or instance fails.

For NAT, a single NAT gateway is an AZ-scoped managed resource. If private subnets in other AZs route to a NAT gateway in one AZ, an AZ outage can break outbound connectivity for workloads that depend on NAT. The resilient pattern is to deploy a NAT gateway in each Availability Zone and configure each private subnet’s route table to use the NAT gateway in the same AZ.

Option A addresses all three: it deploys additional NAT gateways per AZ and updates route tables accordingly, converts RDS to Multi-AZ, and adjusts Auto Scaling to launch across AZs with min and max set to 3 so there are instances running in multiple AZs simultaneously. This ensures the application remains available across AZ failures, meeting the requirement.

Option B replaces NAT with a virtual private gateway, which is used for VPN/Direct Connect connectivity, not for internet egress from private subnets. It does not satisfy the NAT functionality requirement. Although Aurora can provide high availability, the NAT replacement is not correct for the described architecture.

Option C increases operational overhead by replacing NAT gateway with NAT instances, which are self-managed and less resilient without additional design. It also changes the database engine to PostgreSQL unnecessarily and does not directly address the requirement with minimal change.

Option D improves NAT resiliency but only enables RDS automatic backups, which is a durability feature, not a high availability feature. Backups do not provide automatic failover and do not ensure the application will operate across multiple AZs. Also, keeping Auto Scaling min/max at 1 still leaves the application compute layer single-AZ at any given moment.

Therefore, option A is the correct solution.

The correct answer is B. This option uses IAM Identity Center to create permission sets that map to the existing IAM roles in each account. This way, the company can leverage the existing policies and roles that are already configured for the expected activities of each group of users in each account. The company also needs to create a customer managed policy that allows the group to assume the roles and attach it to thepermission set. This policy grants the necessary permissions for IAM Identity Center to assume the roles on behalf of the users. Finally, the company can assign user access to the AWS accounts in IAM Identity Center, which will automatically create IAM users and groups in each account based on the permission sets.

Option A is incorrect because it requires creating new policies in each account and giving them the same name. This is not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option C is incorrect because it requires creating new policies in each account and giving them unique names. This is also not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option D is incorrect because it requires adding the OU to the accounts configuration in IAM Identity Center. This is not supported by IAM Identity Center, which only allows adding individual accounts or all accounts in an organization.

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

B is correct because SageMaker AI multi-model endpoints are designed for hosting a large number of models that use the same ML framework behind shared serving infrastructure. This is exactly the pattern for a SaaS platform with thousands of tenant-specific models. A dedicated endpoint per model would create excessive cost and management overhead. Serverless endpoints reduce instance management but do not solve the cost problem of managing thousands of separate model deployments. Asynchronous inference and batch transform are not appropriate because the requirement includes sub-200 ms latency, which requires real-time inference. AWS documentation states that multi-model endpoints are ideal for hosting many models using the same framework and can provide higher cost savings by sharing resources across models.

For migration planning, AWS provides specific tooling to discover on-premises servers, collect detailed performance and configuration data, and analyze application dependencies. The primary service for this purpose is AWS Application Discovery Service, which integrates with AWS Migration Hub.

The AWS Application Discovery Agent is installed on on-premises physical servers and VMs. It collects detailed information such as CPU, memory, and disk utilization; processes and services running on the system; system configuration details; and network connections between systems. This allows the company to understand how servers communicate, what applications are running, and what dependencies exist between components.

This data is sent to AWS Application Discovery Service and surfaced through AWS Migration Hub, where the company can view discovered servers, group them logically into applications, and analyze migration readiness. Migration Hub can use this collected data to provide recommendations for EC2 instance sizing that are based on actual on-premises utilization metrics, helping choose appropriate EC2 instance types and sizes.

Option C directly describes installing the AWS Application Discovery Agent, using AWS Migration Hub to discover and group servers into applications, and using Migration Hub to generate EC2 instance recommendations. This aligns with AWS’s migration assessment tooling and meets all the stated goals: performance measurement, system configuration listing, network connection understanding, dependency analysis, and instance sizing recommendations.

Option A uses AWS Systems Manager Agent and Application Manager. While Systems Manager can manage servers and collect some information, it is not the primary tool for detailed migration discovery, networking dependency mapping, and migration-focused analysis. It also suggests using AWS Pricing Calculator for instance recommendations, which requires manual input and does not automatically derive recommendations from observed utilization and dependencies.

Option B suggests using the Amazon Inspector agent to gather performance and usage information. Amazon Inspector is designed for vulnerability and security assessments, not for detailed migration discovery or dependency mapping. Additionally, AWS Compute Optimizer currently bases its recommendations on usage metrics from AWS resources, not on-premises servers.

Option D uses the CloudWatch agent to collect metrics and then Migration Hub and Compute Optimizer. The CloudWatch agent is not the standard agent for migration discovery and dependency analysis of on-premises workloads. Also, Compute Optimizer focuses on optimization of existing AWS resources rather than creating recommendations based on on-premises utilization.

Therefore, installing the AWS Application Discovery Agent and using AWS Migration Hub (option C) is the correct solution to satisfy all the requirements for migration assessment and EC2 sizing recommendations.

CloudFormation cannot delete an S3 bucket that contains objects. When the developers attempt to delete temporary stacks, the delete operation fails if the associated S3 bucket still has objects. Because the bucket name is derived from the stack name, failed deletions can also prevent re-creation of stacks with the same names until the bucket is manually emptied and deleted. This causes friction for development and continuous integration workflows.

To resolve this, the solution must ensure that, during stack deletion, all objects are removed from the S3 bucket so that CloudFormation can delete the bucket successfully. CloudFormation does not natively support automatic deletion of bucket contents. However, CloudFormation custom resources can be used to perform custom actions during stack lifecycle events.

Option A proposes associating a Lambda function with a CloudFormation custom resource. The custom resource can be invoked during stack deletion to list and delete all objects (and object versions if versioning is enabled) in the specified bucket. Once the bucket is emptied by the Lambda function, the standard CloudFormation deletion process can successfully delete the bucket resource.

In addition to emptying the bucket, the IAM role used by CloudFormation must have permissions to delete objects in the bucket. If CloudFormation uses an execution role that lacks s3:DeleteObject (and potentially s3:ListBucket) permissions, the bucket-cleanup Lambda function or CloudFormation itself would not be able to remove objects. Option D ensures that CloudFormation operations are invoked by a role that has s3:DeleteObject permissions on bucket objects, enabling the custom resource to perform the deletions.

Option B is incorrect because, although DeletionPolicy can be set to Delete, there is no such capability as CAPABILITY_DELETE_NONEMPTY in CloudFormation, and DeletionPolicy alone does not override the S3 service requirement that buckets must be empty before deletion.

Option C’s Retain DeletionPolicy leaves the bucket behind when the stack is deleted. While an external AWS Config rule could clean up stale buckets, it complicates the design and does not directly solve the problem of re-creating stacks with the same bucket name in a predictable, immediate way.

Option E configures a bucket policy to grant s3:DeleteObject permissions, but this is not sufficient alone. CloudFormation still must assume a role with the appropriate permissions. Bucket policy and IAM role permissions must both be considered, but the key action to resolve the deletion failure is to explicitly empty the bucket through a custom resource, as in option A, and ensure the execution role has delete permissions, as in option D.

Therefore, implementing a Lambda-backed custom resource to empty the S3 bucket during stack deletion (option A) and ensuring the CloudFormation execution role has s3:DeleteObject permissions (option D) resolves the deletion errors and allows developers to delete and recreate stacks freely.

A and C are correct. Aurora PostgreSQL can store patient data in an encrypted database and can enforce TLS for encrypted connections in transit. For tamper-resistant auditing, Aurora Database Activity Streams are the stronger choice because they monitor database activity and send the stream to Amazon Kinesis for external consumption. AWS documentation states that activity streams monitor and report activities and that the activity stream is collected and transmitted to Kinesis. Sending those audit records to Amazon S3 and enabling S3 Object Lock in compliance mode provides immutable retention. Compliance mode prevents protected object versions from being overwritten or deleted by any user, including the root user, during the retention period. pgAudit logs in CloudWatch are useful, but they do not provide the same database activity stream control and immutability pattern.

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_inheritance_auth.html

The company should use Migration Evaluator to generate a list of servers and build a report for a business case. The company should use AWS Migration Hub to view the portfolio and use AWS Application Discovery Service to gain an understanding of application dependencies. This solution will meet the requirements because Migration Evaluator is a migration assessment service that helps create a data-driven business case for AWS cloud planning and migration. Migration Evaluator provides a clear baseline of what the company is running today and projects AWS costs based on measured on-premises provisioning and utilization1. Migration Evaluator can use an agentless collector to conduct broad-based discovery or securely upload exports from existinginventory tools2. Migration Evaluator integrates with AWS Migration Hub, which is a service that provides a single location to track the progress of applicationmigrations across multiple AWS and partner solutions3. Migration Hub also supports AWS Application Discovery Service, which is a service that helps systems integrators quickly and reliably plan application migration projects by automatically identifying applications running in on-premises data centers, their associated dependencies, and their performance profile4.

https://aws.amazon.com/migration-evaluator/

https://docs.aws.amazon.com/migration-evaluator/latest/userguide/what-is.html

https://aws.amazon.com/migration-hub/

https://aws.amazon.com/application-discovery/

https://aws.amazon.com/server-migration-service/

https://aws.amazon.com/dms/

https://docs.aws.amazon.com/controltower/latest/userguide/what-is-control-tower.html

https://aws.amazon.com/application-migration-service/

https://aws.amazon.com/storagegateway/

The correct answer is A, D, and F. Existing AWS accounts can be invited into an AWS Organizations organization, and SCPs are the correct organization-level mechanism to enforce preventive controls such as required tagging. Because OUs contain accounts, not individual EC2 instances, development workloads must be recreated or migrated into an account that is placed under the development OU. Amazon Inspector is the correct service for automated EC2 vulnerability scanning, and Security Hub can aggregate and centralize findings across an organization. For patching, AWS Systems Manager Patch Manager can be managed across an organization through delegated administration and organization-wide patch policies, which reduces operational overhead compared with configuring each account separately. Trusted Advisor is not the right vulnerability scanning service, and configuring Patch Manager separately in every account creates unnecessary administration.

Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html