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

 The company should configure a cross-Region read replica for the RDS database in the new Region. The company should change the Route 53 record to latency-based routing to connect to the API Gateway API. This solution will meet the requirements because a cross-Region read replica is a feature that enables you to create a MariaDB, MySQL, Oracle, PostgreSQL, or SQL Server read replica in a different Region from the source DB instance. You can use cross-Region read replicas to improve availability and disaster recovery, scale out globally, or migrate an existing database to a new Region1. By creating a cross-Region read replica for the RDS database in the new Region, the company can have a standby copy of its primary database that can serve read-only traffic from users in Europe. A latency-based routing policy is a feature that enables you to route traffic based on the latency between your users and your resources. You can use latency-based routing to route traffic to the resource that provides the best latency2. By changing the Route 53 record to latency-based routing, the company can minimize latency for users who download reports by connecting them to the API Gateway API in the Region that provides the best response time.

The other options are not correct because:

Using AWS Database Migration Service (AWS DMS) to replicate the primary database in the original Region to the database in the new Region would not be as cost-effective or simple as using a cross-Region read replica. AWS DMS is a service that enables you to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to perform one-time migrations or continuous data replication with high availability and consolidate databases intoa petabyte-scale data warehouse3. However, AWS DMS requires more configuration and management than creating a cross-Region read replica, which is fully managed by Amazon RDS. AWS DMS also incurs additional charges for replication instances and tasks.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not help with disaster recovery or minimizing latency. 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 client. It is useful forbuilding applications that interact with Amazon Redshift, but not for replicating or recovering data from an RDS database.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not help with disaster recovery or minimizing latency. AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use AWS Data Exchange to subscribe to a diverse selection of third-party data products or offer your own data products to other AWS customers. 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 data. It is useful for sharing query results and views with other users, but not for replicating or recovering data from an RDS database.

The requirement is not just foundation-model evaluation. It is full agent release validation: task completion, correct tool selection, final response quality, golden traces, continuous monitoring, and human audit. Amazon Bedrock AgentCore Evaluations is designed for automated assessment of agent and tool performance before and after deployment. Built-in evaluators can be used for common evaluation scenarios, custom evaluators can test organization-specific criteria, and online evaluations continuously monitor live traffic. That directly supports CI/CD quality gates and post-deployment degradation detection with less custom engineering. Option A creates a custom evaluation pipeline, increasing operational overhead. Option B evaluates only the underlying model, not the agent’s tool behavior. Option D covers online monitoring but misses pre-deployment golden-trace validation and custom evaluator use in the pipeline.

This solution will allow the detection logic to be run as soon as the image is uploaded to the S3 bucket, before it is served to users via the CloudFront distribution. This way, the detection logic can quickly identify any corrupted images and prevent them from being served to users, minimizing latency between ingestion and serving.

Converting VPC flow logs to store in Apache Parquet format and specifying hourly partitions significantly improves query performance and reduces storage space usage. Apache Parquet is a columnar storage file format optimized for analytical queries, allowing Athena to scan less data and improve query performance. Partitioning logs by hour further enhances query efficiency by limiting the amount of data scanned during queries, addressing the issue of degrading performance over time due to the growing volume of ingested logs.

AWS Documentation on VPC Flow Logs and Amazon Athena provides insights into configuring VPC flow logs in Apache Parquet format and using Athena for querying log data. This approach is recommended for efficient log analysis and storage optimization.

an AWS Lambda function in the backup region to promote the read replica and modify the Auto Scaling group values, and then configuring Route 53 with a health check that monitors the web application and sends an Amazon SNS notification to the Lambda function when the health check status is unhealthy. Finally, the application ' s Route 53 record should be updated with a failover policy that routes traffic to the ALB in the backup region when a health check failure occurs. This approach provides automatic failover to the backup region when a health check failure occurs, reducing the RTO to less than 15 minutes. Additionally, this approach is cost-effective as it does not require an active-active strategy.

Creating an internal ALB and configuring it as a PrivateLink endpoint service enables private connectivity between internal applications and the metadata API, ensuring that traffic does not traverse the public internet.

Internal ALB: Ensures traffic stays within the AWS network and is not exposed publicly.

PrivateLink endpoint service: Provides secure, private access to the ALB from the internal EC2 instances in other AWS accounts.

Traffic stays within the AWS global network, leveraging AWS security best practices and meeting the new policy requirements for no public internet exposure.This approach is secure, scalable, and minimizes management complexity compared to API Gateway solutions.

The requirement is to continuously scan all AWS resources in this solution for security vulnerabilities with the least operational overhead. The environment includes Amazon EKS worker nodes in a managed node group and an Amazon ECR repository that stores container images used by the EKS cluster.

Amazon Inspector is a managed vulnerability scanning service that integrates directly with multiple AWS resource types. It can automatically discover Amazon EC2 instances (including EKS managed node group instances) and perform continuous vulnerability assessments against them, using software inventory and known CVE databases. Inspector also integrates with Amazon ECR to scan container images for vulnerabilities when images are pushed to the repository and periodically thereafter, providing continuous assessment of image security posture without requiring separate scanners or infrastructure.

By activating Amazon Inspector for the account and region, the service will automatically start tracking supported resources, including EKS-managed EC2 instances and ECR repositories, and will begin scanning them for known vulnerabilities. Findings are provided through the Inspector console and can be integrated with other services such as AWS Security Hub or Amazon EventBridge for further aggregation and automation, but no separate infrastructure deployment or maintenance is required.

Option B, therefore, provides end-to-end vulnerability scanning for both the EKS nodes and the ECR images with minimal operational overhead, because it leverages a fully managed AWS service that automatically discovers and scans supported resources.

Option A is not correct because AWS Security Hub does not itself perform vulnerability scanning. Security Hub aggregates and normalizes security findings from various AWS services, including Amazon Inspector, GuardDuty, and others. It is a security posture management and aggregation service, not a vulnerability scanner.

Option C introduces additional operational overhead by requiring the company to provision, configure, secure, and maintain a separate EC2 instance running a third-party vulnerability scanning tool. While ECR basic scan-on-push can detect some image vulnerabilities, the EC2-based scanner must be managed, updated, and scaled by the customer, which violates the requirement for the least operational overhead when a fully managed alternative exists.

Option D is incorrect because the Amazon CloudWatch agent is used for metrics and log collection from EC2 instances and other environments; it does not perform vulnerability scanning or CVE analysis. Configuring CloudWatch for monitoring and logging does not meet the requirement to continuously scan for security vulnerabilities. ECR basic scans also provide only image-level scanning and do not cover the EKS nodes themselves.

Therefore, enabling Amazon Inspector to scan both the EKS nodes and the ECR repository, as described in option B, is the solution that meets the continuous vulnerability scanning requirement with the least operational overhead.

Using AWS IoT Core to receive the vehicle data will enable connecting the smart vehicles to the cloud using the MQTT protocol1. AWS IoT Core is a platform that enables you to connect devices to AWS Services and other devices, secure data and interactions, process and act upon device data, and enable applications to interact with devices even when they are offline2. Configuring rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3 will enable processing and storing the vehicle data in a scalable and reliable way3. Amazon Kinesis Data Firehose is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3. Creating an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies will enable analyzing the vehicle data using SQL queries or Apache Flink applications. Amazon Kinesis Data Analytics is a fully managed service that enables you to process and analyze streaming data using SQL or Java.

Amazon Simple Notification Service (Amazon SNS) is a fully managed pub/sub messaging service that enables users to send messages to multiple subscribers1. Users can sendevent details to an Amazon SNS topic and configure an AWS Lambda function as a subscriber to the SNS topic to process the events. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. Users can add an on- failure destination to the function and set an Amazon Simple Queue Service (Amazon SQS) queue as the target. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications3. This way, if a processing error occurs, the event will move into the separate queue for review.

Option B is incorrect because publishing events to an Amazon SQS queue and creating an Amazon EC2 Auto Scaling group will not have the ability to scale in and out based on the number of events that the solution receives. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud. Auto Scaling is a feature that helps users maintain application availability and allows them to scale their EC2 capacity up or down automatically according to conditions they define. However, for this use case, using SQS and EC2 will not take advantage of the serverless capabilities of Lambda and SNS.

Option C is incorrect because writing events to an Amazon DynamoDB table and configuring a DynamoDB stream for the table will not have the ability to move events into a separate queue for review if a processing error occurs. Amazon DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB Streams is a feature that captures data modification events in DynamoDB tables. Users can configure the stream to invoke a Lambda function, but they cannot configure an on-failure destination for the function.

Option D is incorrect because publishing events to an Amazon EventBridge event bus and setting an Application Load Balancer (ALB) as the event bus target will not have the ability to move events into a separate queue for review if a processing error occurs. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. An ALB is a load balancer that distributes incoming application traffic across multiple targets, such as EC2 instances, containers, IP addresses, Lambda functions, and virtual appliances. Users can configure EventBridge to retry events, but they cannot configure an on-failure destination for the ALB.

https://docs.aws.amazon.com/global-accelerator/latest/dg/about-accelerators.alb-accelerator.html Option A is wrong. AWS WAF does not support associating with NLB. https://docs.aws.amazon.com/waf/latest/developerguide/waf-chapter.html Option B is wrong. An ALB does not support an Elastic IP address.https://aws.amazon.com/elasticloadbalancing/features/

The correct design is to use CloudWatch usage metrics and service quota metric math. AWS publishes usage metrics for some services in the AWS/Usage namespace, and these metrics correspond to service quotas. CloudWatch can use the SERVICE_QUOTA metric math function to compare current usage against the quota and trigger an alarm when usage approaches a defined threshold. This is exactly the requirement: notify the team when Fargate task usage reaches 80% of the maximum allowed tasks. Amazon SNS is the standard low-overhead notification target for CloudWatch alarms. Polling ECS with Lambda is unnecessary custom engineering. AWS Config evaluates resource compliance, not quota utilization trends. Monitoring each ECS service’s sample count would not accurately represent account-level Fargate task quota consumption. (AWS Documentation)

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Amazon Connect is a cloud-based contact center service that allows you to set up a virtual call center for your business. It provides an easy-to-use interface for managing customer interactions through voice and chat. Amazon Connect integrates with other AWS services, such as Amazon S3 and Amazon Kinesis, to help you collect, store, and analyze customer data for insights into customer behavior and trends. On the other hand, Amazon Pinpoint is a marketing automation and analytics service that allows you to engage with your customers across different channels, such as email, SMS, push notifications, and voice. It helps you create personalized campaigns based on userbehavior and enables you to track user engagement and retention. While both services allow you to communicate with your customers, they serve different purposes. Amazon Connect is focused on customer support and service, while Amazon Pinpoint is focused on marketing and engagement.