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Amazon SAP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Design for New Solutions	31%	- Multi-tier architecture 1. Application tier 2. Data tier considerations 3. Web tier design - Network design 1. Network segmentation 2. Subnet planning 3. Security groups and NACLs 4. VPC architecture - High availability and fault tolerance 1. Multi-AZ deployments 2. Backup and restore 3. Disaster recovery strategies - Database migration 1. Database types and selection 2. Schema Conversion Tool (SCT) 3. AWS DMS (Database Migration Service) - Microservices patterns 1. Event-driven architecture 2. Serverless architectures (Lambda) 3. Containers and ECS/EKS - Cloud Adoption Readiness 1. Migration strategy assessment 2. 6 Rs of migration - Cost optimization at design phase 1. Right-sizing resources 2. Reserved Instances and Savings Plans 3. Spot instances
Topic 2: Continuous Improvement for Existing Solutions	23%	- Security hardening 1. Identity and access management 2. Encryption strategies 3. Security monitoring - Modernization strategies 1. Serverless transformation 2. Re-architecting monolithic applications 3. Containerization - Performance optimization 1. Database optimization 2. Caching strategies (CloudFront, ElastiCache) 3. CDN implementation - Reviewing existing architectures 1. Performance efficiency pillar 2. Operational excellence pillar 3. Security pillar 4. Reliability pillar 5. Cost optimization pillar 6. AWS Well-Architected Framework - Operational excellence improvements 1. CI/CD pipelines 2. Monitoring and logging (CloudWatch) 3. Infrastructure as Code (CloudFormation, Terraform)
Topic 3: Cost Control	18.5%	- Cost optimization strategies 1. Cost categories 2. Cost allocation tags 3. AWS Cost Explorer 4. AWS Budgets - Reserved capacity planning 1. Reserved Instances 2. Savings Plans 3. Capacity Reservations - Resource efficiency 1. Managed services 2. Auto Scaling 3. Serverless optimization - Monitoring and controlling costs 1. Anomaly detection 2. Budget alerts - Rightsizing recommendations
Topic 4: Migration Planning	15%	- Validation and testing post-migration - Migration strategies 1. Rehosting (lift-and-shift) 2. Hybrid migration 3. Replatforming 4. Repurchasing 5. Refactoring - Migration tools and services 1. AWS Server Migration Service 2. AWS DataSync 3. AWS Application Migration Service 4. AWS Migration Hub 5. AWS Transfer Family - Data migration considerations 1. Snow family devices 2. Large-scale data transfer
Topic 5: Design for Organizational Complexity	12.5%	- Multi-account AWS environments - Cross-account strategies and AWS organizations 1. Service Control Policies (SCPs) 2. Organizational Units (OUs) 3. AWS Control Tower - Networks 1. AWS Direct Connect 2. VPN connections 3. AWS Transit Gateway 4. Hybrid DNS architecture

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Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q178-Q183):

NEW QUESTION # 178
A company runs an loT application in the AWS Cloud. The company has millions of sensors that collect data from houses in the United States. The sensors use the MOTT protocol to connect and send data to a custom MQTT broker. The MQTT broker stores the data on a single Amazon EC2 instance. The sensors connect to the broker through the domain named iot.example.com. The company uses Amazon Route 53 as its DNS service. The company stores the data in Amazon DynamoDB.
On several occasions, the amount of data has overloaded the MOTT broker and has resulted in lost sensor data. The company must improve the reliability of the solution.
Which solution will meet these requirements?

A. Set up AWS loT Core to receive the sensor data. Create and configure a custom domain to connect to AWS loT Core. Update the DNS record in Route 53 to point to the AWS loT Core Data-ATS endpoint.
Configure an AWS loT rule to store the data.
B. Create an Application Load Balancer (ALB) and an Auto Scaling group for the MOTT broker. Use the Auto Scaling group as the target for the ALB. Update the DNS record in Route 53 to an alias record.
Point the alias record to the ALB. Use the MQTT broker to store the data.
C. Set up AWS loT Greengrass to receive the sensor data. Update the DNS record in Route 53 to point to the AWS loT Greengrass endpoint. Configure an AWS loT rule to invoke an AWS Lambda function to store the data.
D. Create a Network Load Balancer (NLB). Set the MQTT broker as the target. Create an AWS Global Accelerator accelerator. Set the NLB as the endpoint for the accelerator. Update the DNS record in Route 53 to a multivalue answer record. Set the Global Accelerator IP addresses as values. Use the MQTT broker to store the data.

Answer: B

Explanation:
Explanation
it describes a solution that uses an Application Load Balancer (ALB) and an Auto Scaling group for the MQTT broker. The ALB distributes incoming traffic across the instances in the Auto Scaling group and allows for automatic scaling based on incoming traffic. The use of an alias record in Route 53 allows for easy updates to the DNS record without changing the IP address. This solution improves the reliability of the MQTT broker by allowing it to automatically scale based on incoming traffic, reducing the likelihood of lost data due to broker overload.

NEW QUESTION # 179
A company is using Amazon Aurora MySQL for a customer relationship management (CRM) application. The application requires frequent maintenance on the database and the Amazon EC2 instances on which the application runs. For AWS Management Console access, the system administrators authenticate against AWS Identity and Access Management (IAM) using an internal identity provider. For database access, each system administrator has a user name and password that have previously been configured within the database. A recent security audit revealed that the database passwords are not frequently rotated. The company wants to replace the passwords with temporary credentials using the company's existing AWS access controls.
Which set of options will meet the company's requirements?

A. Create a new AWS Systems Manager Parameter Store entry for each database password.
Enable parameter expiration to invoke an AWS Lambda function to perform password rotation by updating the parameter value.
Create an IAM policy allowing each system administrator to retrieve their current password from the Parameter Store.
Use the AWS CLI to retrieve credentials when connecting to the database.
B. Enable IAM database authentication on the database.
Attach an IAM policy to each system administrator's role to map the role to the database user name.
Install the Amazon Aurora SSL certificate bundle to the system administrators' certificate trust store.
Use the AWS CLI to generate an authentication token used when connecting to the database.
C. Enable IAM database authentication on the database.
Configure the database to use the IAM identity provider to map the administrator roles to the database user.
Install the Amazon Aurora SSL certificate bundle to the system administrators' certificate trust store.
Use the AWS CLI to generate an authentication token used when connecting to the database.
D. Create a new AWS Secrets Manager entry for each database password.
Configure password rotation for each secret using an AWS Lambda function in the same VPC as the database cluster.
Create an IAM policy allowing each system administrator to retrieve their current password.
Use the AWS CLI to retrieve credentials when connecting to the database.

Answer: B

Explanation:
To allow an IAM user or role to connect to your DB cluster, you must create an IAM policy. After that, you attach the policy to an IAM user or role.
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/UsingWithRDS.IAMDBAuth.I AMPolicy.html

NEW QUESTION # 180
A company has a web application that allows users to upload short videos. The videos are stored on Amazon EBS volumes and analyzed by custom recognition software for categorization.
The website contains stat c content that has variable traffic with peaks in certain months. The architecture consists of Amazon EC2 instances running in an Auto Scaling group for the web application and EC2 instances running in an Auto Scaling group to process an Amazon SQS queue The company wants to re- architect the application to reduce operational overhead using AWS managed services where possible and remove dependencies on third-party software.
Which solution meets these requirements?

A. Use Amazon ECS containers for the web application and Spot Instances for the Auto Scaling group that processes the SQS queue. Replace the custom software with Amazon Recognition to categorize the videos.
B. Host the web application in Amazon S3. Store the uploaded videos in Amazon S3. Use S3 event notifications to publish events to the SQS queue Process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API to categorize the videos.
C. Use AWS Elastic Beanstalk to launch EC2 instances in an Auto Scaling group for the web application and launch a worker environment to process the SQS queue Replace the custom software with Amazon Rekognition to categorize the videos.
D. Store the uploaded videos n Amazon EFS and mount the file system to the EC2 instances for Te web application. Process the SOS queue with an AWS Lambda function that calls the Amazon Rekognition API to categorize the videos.

Answer: B

Explanation:
* Option C is correct because hosting the web application in Amazon S3, storing the uploaded videos in Amazon S3, and using S3 event notifications to publish events to the SQS queue reduces the operational overhead of managing EC2 instances and EBS volumes. Amazon S3 can serve static content such as HTML, CSS, JavaScript, and media files directly from S3 buckets. Amazon S3 can also trigger AWS Lambda functions through S3 event notifications when new objects are created or existing objects are updated or deleted. AWS Lambda can process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API to categorize the videos. This solution eliminates the need for custom recognition software and third-party dependencies345 References:
1: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html
2: https://aws.amazon.com/efs/pricing/
3: https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html
4: https://docs.aws.amazon.com/AmazonS3/latest/userguide/NotificationHowTo.html
5: https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html
6: https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/Welcome.html

NEW QUESTION # 181
A company is running an event ticketing platform on AWS and wants to optimize the platform's cost-effectiveness. The platform is deployed on Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 and is backed by an Amazon RDS for MySQL DB instance. The company is developing new application features to run on Amazon EKS with AWS Fargate.
The platform experiences infrequent high peaks in demand. The surges in demand depend on event dates.
Which solution will provide the MOST cost-effective setup for the platform?

A. Purchase Standard Reserved Instances for the EC2 instances that the EKS cluster uses in its baseline load. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet predicted peak load for the year.
B. Purchase EC2 Instance Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.
C. Purchase Compute Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.
D. Purchase Compute Savings Plans for the predicted medium load of the EKS cluster. Scale the cluster with On-Demand Capacity Reservations based on event dates for peaks. Purchase 1-year No Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale out database read replicas during peaks.

Answer: D

Explanation:
Explanation
They all mention using spot instances and EKS based on EC2. A spot instance is not appropriate for a production server and the company is developing new application designed for AWS Fargate, which means we must plan the future cost improvement including AWS Fargate.
https://aws.amazon.com/savingsplans/compute-pricing/

NEW QUESTION # 182
A life sciences company is using a combination of open source tools to manage data analysis workflows and Docker containers running on servers in its on-premises data center to process genomics data Sequencing data is generated and stored on a local storage area network (SAN), and then the data is processed. The research and development teams are running into capacity issues and have decided to re-architect their genomics analysis platform on AWS to scale based on workload demands and reduce the turnaround time from weeks to days The company has a high-speed AWS Direct Connect connection Sequencers will generate around 200 GB of data for each genome, and individual jobs can take several hours to process the data with ideal compute capacity. The end result will be stored in Amazon S3. The company is expecting 10-15 job requests each day Which solution meets these requirements?

A. Use AWS Data Pipeline to transfer the sequencing data to Amazon S3 Use S3 events to trigger an Amazon EC2 Auto Scaling group to launch custom-AMI EC2 instances running the Docker containers to process the data
B. Use regularly scheduled AWS Snowball Edge devices to transfer the sequencing data into AWS When AWS receives the Snowball Edge device and the data is loaded into Amazon S3 use S3 events to trigger an AWS Lambda function to process the data
C. Use an AWS Storage Gateway file gateway to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Batch job that runs on Amazon EC2 instances running the Docker containers to process the data
D. Use AWS DataSync to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Lambda function that starts an AWS Step Functions workflow Store the Docker images in Amazon Elastic Container Registry (Amazon ECR) and trigger AWS Batch to run the container and process the sequencing data

Answer: D

Explanation:
Explanation
AWS DataSync can be used to transfer the sequencing data to Amazon S3, which is a more efficient and faster method than using Snowball Edge devices. Once the data is in S3, S3 events can trigger an AWS Lambda function that starts an AWS Step Functions workflow. The Docker images can be stored in Amazon Elastic Container Registry (Amazon ECR) and AWS Batch can be used to run the container and process the sequencing data.

NEW QUESTION # 183
......

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