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

Amazon S3 can generate server access logs that record detailed information about each request, including requester, bucket, key, operation, time, and status. These logs are written as objects to an S3 bucket. To analyze access patterns, the simplest and most serverless approach is to use Amazon Athena directly on those logs without building ingestion pipelines or databases.

Option B enables S3 server access logging and then creates an Athena external table over the log bucket. AWS provides standard log formats and even example schemas for S3 access logs. The analytics team can run ad hoc SQL queries to count the number of accesses per object per time period, filter by user, and perform aggregations, all without provisioning compute or managing databases.

Option A requires ingesting logs into Aurora, which adds ETL complexity and ongoing database management. Option C requires a Lambda function for every access event plus DB writes, which is more complex and potentially expensive at scale. Option D uses CloudWatch Logs and Managed Flink, which is more suited for streaming analytics and is significantly more complex than necessary for monthly summary reports.

Therefore, Option B provides the required analysis with the least development and operational effort.

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The correct answer is B because AWS Config can continuously evaluate the account-level S3 Block Public Access configuration by using a managed rule. When the configuration becomes noncompliant, AWS Config can trigger automatic remediation through an AWS Systems Manager Automation runbook to restore the required settings.

This solution directly satisfies both requirements:

Detect changes to account-level S3 public access settings.

Automatically revert those changes so that all public access is blocked again.

Why the other options are incorrect:

A. Security Hub is primarily for aggregated security findings and controls visibility. It is not the most direct or standard service for continuous configuration evaluation with automatic remediation of this specific setting.

C. An SCP can restrict permissions, but it does not automatically detect and revert changes to S3 Block Public Access settings.

D. Amazon Macie is for discovering and protecting sensitive data, not for continuously enforcing and remediating S3 Block Public Access account settings.

The most cost-effective solution to meet the RTO and RPO requirements is option B. This option involves copying the CloudFormation templates to an Amazon S3 bucket in the DR Region, configuring Aurora automated backup Cross-Region Replication, and configuring ECR Cross-Region Replication. In the event of a disaster, the CloudFormation template with the most recent Aurora snapshot and the Docker image from the local ECR repository can be used to launch a new CloudFormation stack in the DR Region. This approach avoids the need to build Docker images from the Dockerfile, which can sometimes take more than 2 hours, thus meeting the RTO requirement. Additionally, the use of automated backups and replication ensures that the RPO of 8 hours is met.

AWS Documentation on Disaster Recovery: Plan for Disaster Recovery (DR) - Reliability Pillar

AWS Blog on Establishing RPO and RTO Targets: Establishing RPO and RTO Targets for Cloud Applications

AWS Documentation on ECR Cross-Region Replication: Amazon ECR Cross-Region Replication

AWS Documentation on Aurora Cross-Region Replication: Replicating Amazon Aurora DB Clusters Across AWS Regions

ALB natively supports WebSocket protocols and sticky sessions via target group session affinity. Deploying ALBs in multiple regions with cross-zone load balancing ensures high availability and fault tolerance.

Using Route 53 latency-based routing allows users worldwide to connect to the lowest latency region, minimizing delay.

NLBs do not support sticky sessions and WebSocket protocol as well as ALBs do. Combining ALBs and NLBs (Option D) increases complexity. CloudFront (Option C) does not natively support WebSocket sticky sessions.

Option A is the simplest, most effective solution meeting all requirements with least operational overhead.

it involves adding an IPv6 CIDR block to the VPC and subnets for the ALB and specifying the dualstack IP address type on the ALB listener. This allows the ALB to listen on both IPv4 and IPv6 addresses, and forward requests to the EC2 instances that are added as targets to the target group associated with the ALB.

Amazon CloudWatch Logs now supports data protection policies that can mask sensitive data in logs at ingestion time using custom data identifiers. By creating a custom data identifier matching the employee ID pattern (Emp-\d{6}), the administrator can mask those patterns in new log entries automatically.

Assigning an IAM policy that denies the logs:Unmask action to engineers prevents them from seeing unmasked sensitive data, enforcing least privilege access.

This approach is operationally efficient because it uses built-in CloudWatch Logs data protection capabilities without the need for complex custom code or manual log filtering.

Option C requires writing and managing Lambda functions for log transformation, increasing operational overhead. Option D is more complex and reactive rather than preventing the sensitive data exposure. Option B incorrectly references NotAction and managed identifiers that may not match the custom pattern.

The core problem described is deployment inconsistency and lack of reliability caused by using AWS Systems Manager Run Command for application deployment. Run Command executes ad hoc commands and does not provide deployment orchestration, version tracking, lifecycle hooks, or health-based traffic control, which commonly leads to drift between EC2 instances.

The most reliable AWS-native solution is to adopt AWS CodePipeline combined with AWS CodeDeploy . Option B introduces a structured CI/CD pipeline with a clear build stage followed by a test stage , ensuring that only tested artifacts progress to deployment. Sequential build and test stages are preferred for reliability and deterministic behavior, especially when test results must gate deployments.

Option C is essential because AWS CodeDeploy is the service specifically designed to deploy application revisions consistently across EC2 fleets. By creating a CodeDeploy application and deployment group and integrating it with the ALB, deployments gain support for lifecycle events, health checks, instance synchronization, and automatic rollback. This ensures that all EC2 instances receive the same application version and that traffic is managed safely during deployments.

Option A introduces unnecessary parallelism that does not address the core issue. Option D adds excessive complexity with Lambda orchestration. Option E incorrectly replaces CodeArtifact with S3 without addressing deployment reliability.

Therefore, combining CodePipeline (B) with CodeDeploy and ALB integration (C) provides consistent, repeatable, and reliable deployments aligned with AWS best practices.

To achieve multi-Region high availability for a serverless API-based application, the architecture must be duplicated in the second Region and supported by a multi-Region data layer and appropriate DNS routing. Option A provides the required multi-Region data layer using DynamoDB global tables , which replicate data automatically and asynchronously between Regions. This allows reads and writes in either Region and prevents cross-Region latency bottlenecks.

Option C ensures that the compute components—API Gateway REST API and AWS Lambda functions—are deployed in both Regions. Each Region operates independently, providing true active-active redundancy and allowing Route 53 to route traffic directly to either Region without dependency on the other.

Option E selects Route 53 latency-based routing , which directs each user to the Region with the lowest round-trip latency. This meets the requirement to route traffic based on the Region providing the best response time rather than only providing active-passive failover.

Option D (failover routing) supports active-standby topologies, not active-active. Option B creates a global secondary index, which does not provide multi-Region replication. Option F (multivalue routing) distributes traffic randomly and does not account for latency or Region placement.

Thus, combining DynamoDB global tables, duplicated serverless architecture, and latency-based routing fully satisfies the requirements.

The requirement is classic attribute-based access control (ABAC) : users should only access EC2 resources whose resource tag matches the user’s department attribute coming from IAM Identity Center (mapped into a principal/session tag like aws:PrincipalTag/department).

For EC2 resource authorization using tags, the correct pattern is to compare the EC2 resource tag condition key ec2:ResourceTag/department to the principal tag ${aws:PrincipalTag/department} . That is exactly what Option C does.

Why the other options are wrong:

A only checks that the tag key exists (aws:TagKeys) and does not enforce a match between user department and instance department.

B uses aws:ResourceTag/... rather than the EC2-specific tag key typically used for EC2 authorization decisions in EC2 actions (ec2:ResourceTag/...).

D restricts access to resources tagged as d1/d2/d3 but does not ensure the user can only access their own department.

The correct approach to meet the requirements for separate environment pipelines and automatic deployment for test environments is to create two AWS CodePipeline configurations, one for each environment. The production pipeline should have a manual approval step to ensure that changes are reviewed before being deployed to production. A single AWS CodeCommit repository with separate branches for each environment allows for organized and efficient code management. Each CodePipeline retrieves the source code from the appropriate branch in the repository. The deployment step utilizes AWS CloudFormation to deploy the Lambda functions, ensuring that the infrastructure as code is maintained and version-controlled.

AWS Lambda with Amazon API Gateway: Using AWS Lambda with Amazon API Gateway

Tutorial on using Lambda with API Gateway: Tutorial: Using Lambda with API Gateway

AWS CodePipeline automatic deployment: Set Up a Continuous Deployment Pipeline Using AWS CodePipeline

Building a pipeline for test and production stacks: Walkthrough: Building a pipeline for test and production stacks