KHÓA HỌC AWS ACADEMY CLOUD ARCHITECTING

Nội dung khóa học

AWS ACADEMY CLOUD ARCHITECTING

( AWS CERTIFIED SOLUTIONS ARCHITECT – ASSOCIATE )

Part 1: Design Secure Architectures

Module 1: Design secure access to AWS resources.

• Access controls and management across multiple accounts
• AWS federated access and identity services (for example, AWS Identity and Access Management [IAM], AWS Single Sign-On [AWS SSO])
• AWS global infrastructure (for example, Availability Zones, AWS Regions)
• AWS security best practices (for example, the principle of least privilege)

• The AWS shared responsibility model LAB:
• Applying AWS security best practices to IAM users and root users (for example, multi-factor authentication [MFA])
• Designing a flexible authorization model that includes IAM users, groups, roles, and policies
• Designing a role-based access control strategy (for example, AWS Security Token Service [AWS STS], role switching, cross-account access)
• Designing a security strategy for multiple AWS accounts (for example, AWS Control Tower, service control policies [SCPs])
• Determining the appropriate use of resource policies for AWS services
• Determining when to federate a directory service with IAM roles

Module 2: Design secure workloads and applications.

• Application configuration and credentials security
• AWS service endpoints
• Control ports, protocols, and network traffic on AWS
• Secure application access
• Security services with appropriate use cases (for example, Amazon Cognito, Amazon GuardDuty, Amazon Macie)
• Threat vectors external to AWS (for example, DDoS, SQL injection) LAB:
• Designing VPC architectures with security components (for example, security groups, route tables, network ACLs, NAT gateways)
• Determining network segmentation strategies (for example, using public subnets and private subnets)
• Integrating AWS services to secure applications (for example, AWS Shield, AWS WAF, AWS SSO, AWS Secrets Manager)
• Securing external network connections to and from the AWS Cloud (for example, VPN, AWS Direct Connect)

Module 3: Determine appropriate data security controls.

• Data access and governance
• Data recovery 
• Data retention and classification

• Encryption and appropriate key management LAB:
• Aligning AWS technologies to meet compliance requirements
• Encrypting data at rest (for example, AWS Key Management Service [AWS KMS])
• Encrypting data in transit (for example, AWS Certificate Manager [ACM] using TLS)
• Implementing access policies for encryption keys
• Implementing data backups and replications
• Implementing policies for data access, lifecycle, and protection
• Rotating encryption keys and renewing certificates

Part 2: Design Resilient Architectures

Module 1: Design scalable and loosely coupled architectures.

• API creation and management (for example, Amazon API Gateway, REST API)
• AWS managed services with appropriate use cases (for example, AWS Transfer Family, Amazon Simple Queue Service [Amazon SQS], Secrets Manager)
• Caching strategies
• Design principles for microservices (for example, stateless workloads compared with stateful workloads)
• Event-driven architectures
• Horizontal scaling and vertical scaling
• How to appropriately use edge accelerators (for example, content delivery network [CDN])
• How to migrate applications into containers
• Load balancing concepts (for example, Application Load Balancer)
• Multi-tier architectures
• Queuing and messaging concepts (for example, publish/subscribe)
• Serverless technologies and patterns (for example, AWS Fargate, AWS Lambda)
• Storage types with associated characteristics (for example, object, file, block

• The orchestration of containers (for example, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS])
• When to use read replicas
• Workflow orchestration (for example, AWS Step Functions) LAB:
• Designing event-driven, microservice, and/or multi-tier architectures based on requirements
• Determining scaling strategies for components used in an architecture design
• Determining the AWS services required to achieve loose coupling based on requirements
• Determining when to use containers
• Determining when to use serverless technologies and patterns
• Recommending appropriate compute, storage, networking, and database technologies based on requirements
• Using purpose-built AWS services for workloads

Module 2: Design highly available and/or fault-tolerant architectures.

• AWS global infrastructure (for example, Availability Zones, AWS Regions, Amazon Route 53)
• AWS managed services with appropriate use cases (for example, Amazon Comprehend, Amazon Polly)
• Basic networking concepts (for example, route tables)
• Disaster recovery (DR) strategies (for example, backup and restore, pilot light, warm standby, active-active failover, recovery point objective [RPO], recovery time objective [RTO])
• Distributed design patterns
• Failover strategies
• Immutable infrastructure
• Load balancing concepts (for example, Application Load Balancer)
• Proxy concepts (for example, Amazon RDS Proxy)
• Service quotas and throttling (for example, how to configure the service quotas for a workload in a standby environment)
• Storage options and characteristics (for example, durability, replication)
• Workload visibility (for example, AWS X-Ray) LAB:

• Determining automation strategies to ensure infrastructure integrity
• Determining the AWS services required to provide a highly available and/or fault-tolerant architecture across AWS Regions or Availability Zones
• Identifying metrics based on business requirements to deliver a highly available solution
• Implementing designs to mitigate single points of failure
• Implementing strategies to ensure the durability and availability of data (for example, backups)
• Selecting an appropriate DR strategy to meet business requirements
• Using AWS services that improve the reliability of legacy applications and applications not built for the cloud (for example, when application changes are not possible)
• Using purpose-built AWS services for workloads

Part 3: Design High-Performing Architectures

Module 1: Determine high-performing and/or scalable storage solutions.

• Hybrid storage solutions to meet business requirements
• Storage services with appropriate use cases (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS])
• Storage types with associated characteristics (for example, object, file, block) LAB:
• Determining storage services and configurations that meet performance demands
• Determining storage services that can scale to accommodate future needs

Module 2: Design high-performing and elastic compute solutions.

• AWS compute services with appropriate use cases (for example, AWS Batch, Amazon EMR, Fargate)
• Distributed computing concepts supported by AWS global infrastructure and edge services
• Queuing and messaging concepts (for example, publish/subscribe)
• Scalability capabilities with appropriate use cases (for example, Amazon EC2 Auto Scaling, AWS Auto Scaling)
• Serverless technologies and patterns (for example, Lambda, Fargate)
• The orchestration of containers (for example, Amazon ECS, Amazon EKS) LAB:

• Decoupling workloads so that components can scale independently
• Identifying metrics and conditions to perform scaling actions
• Selecting the appropriate compute options and features (for example, EC2 instance types) to meet business requirements
• Selecting the appropriate resource type and size (for example, the amount of Lambda memory) to meet business requirements

Module 3: Determine high-performing database solutions.

• AWS global infrastructure (for example, Availability Zones, AWS Regions)
• Caching strategies and services (for example, Amazon ElastiCache)
• Data access patterns (for example, read-intensive compared with write-intensive)
• Database capacity planning (for example, capacity units, instance types, Provisioned IOPS)
• Database connections and proxies
• Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations)
• Database replication (for example, read replicas)
• Database types and services (for example, serverless, relational compared with non-relational, in-memory)

LAB:

• Configuring read replicas to meet business requirements
• Designing database architectures
• Determining an appropriate database engine (for example, MySQL compared with PostgreSQL)
• Determining an appropriate database type (for example, Amazon Aurora, Amazon DynamoDB)
• Integrating caching to meet business requirements

Module 4: Determine high-performing and/or scalable network architectures.

• Edge networking services with appropriate use cases (for example, Amazon CloudFront, AWS Global Accelerator)
• How to design network architecture (for example, subnet tiers, routing, IP addressing)
• Load balancing concepts (for example, Application Load Balancer)
• Network connection options (for example, AWS VPN, Direct Connect, AWS PrivateLink) LAB:

• Creating a network topology for various architectures (for example, global, hybrid, multi-tier)
• Determining network configurations that can scale to accommodate future needs
• Determining the appropriate placement of resources to meet business requirements
• Selecting the appropriate load balancing strategy

Module 5: Determine high-performing data ingestion and transformation solutions.

• Data analytics and visualization services with appropriate use cases (for example, Amazon Athena, AWS Lake Formation, Amazon QuickSight)
• Data ingestion patterns (for example, frequency)
• Data transfer services with appropriate use cases (for example, AWS DataSync, AWS Storage Gateway)
• Data transformation services with appropriate use cases (for example, AWS Glue)
• Secure access to ingestion access points
• Sizes and speeds needed to meet business requirements
• Streaming data services with appropriate use cases (for example, Amazon Kinesis) LAB:
• Building and securing data lakes
• Designing data streaming architectures
• Designing data transfer solutions
• Implementing visualization strategies
• Selecting appropriate compute options for data processing (for example, Amazon EMR)
• Selecting appropriate configurations for ingestion
• Transforming data between formats (for example, .csv to .parquet)

Part 4: Design Cost-Optimized Architectures Module 1: Design cost-optimized storage solutions. Knowledge of:

• Access options (for example, an S3 bucket with Requester Pays object storage)
• AWS cost management service features (for example, cost allocation tags, multi-account billing)

• AWS cost management tools with appropriate use cases (for example, AWS Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
• AWS storage services with appropriate use cases (for example, Amazon FSx, Amazon EFS, Amazon S3, Amazon EBS)
• Backup strategies
• Block storage options (for example, hard disk drive [HDD] volume types, solid state drive [SSD] volume types)
• Data lifecycles
• Hybrid storage options (for example, DataSync, Transfer Family, Storage Gateway)
• Storage access patterns
• Storage tiering (for example, cold tiering for object storage)
• Storage types with associated characteristics (for example, object, file, block) LAB:
• Designing appropriate storage strategies (for example, batch uploads to Amazon S3 compared with individual uploads)
• Determining the correct storage size for a workload
• Determining the lowest cost method of transferring data for a workload to AWS storage
• Determining when storage auto scaling is required
• Managing S3 object lifecycles
• Selecting the appropriate backup and/or archival solution
• Selecting the appropriate service for data migration to storage services
• Selecting the appropriate storage tier
• Selecting the correct data lifecycle for storage
• Selecting the most cost-effective storage service for a workload

Module 2: Design cost-optimized compute solutions.

• AWS cost management service features (for example, cost allocation tags, multi-account billing)
• AWS cost management tools with appropriate use cases (for example, Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
• AWS global infrastructure (for example, Availability Zones, AWS Regions)

• AWS purchasing options (for example, Spot Instances, Reserved Instances, Savings Plans)
• Distributed compute strategies (for example, edge processing)
• Hybrid compute options (for example, AWS Outposts, AWS Snowball Edge)
• Instance types, families, and sizes (for example, memory optimized, compute optimized, virtualization)
• Optimization of compute utilization (for example, containers, serverless computing, microservices)
• Scaling strategies (for example, auto scaling, hibernation) LAB:
• Determining an appropriate load balancing strategy (for example, Application Load Balancer [Layer 7] compared with Network Load Balancer [Layer 4] compared with Gateway Load Balancer)
• Determining appropriate scaling methods and strategies for elastic workloads (for example, horizontal compared with vertical, EC2 hibernation)
• Determining cost-effective AWS compute services with appropriate use cases (for example, Lambda, Amazon EC2, Fargate)
• Determining the required availability for different classes of workloads (for example, production workloads, non-production workloads)
• Selecting the appropriate instance family for a workload
• Selecting the appropriate instance size for a workload

Module 3: Design cost-optimized database solutions.

• AWS cost management service features (for example, cost allocation tags, multi-account billing)
• AWS cost management tools with appropriate use cases (for example, Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
• Caching strategies
• Data retention policies
• Database capacity planning (for example, capacity units)
• Database connections and proxies
• Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations)

• Database replication (for example, read replicas)
• Database types and services (for example, relational compared with non-relational, Aurora, DynamoDB)

LAB:

• Designing appropriate backup and retention policies (for example, snapshot frequency)
• Determining an appropriate database engine (for example, MySQL compared with PostgreSQL)
• Determining cost-effective AWS database services with appropriate use cases (for example, DynamoDB compared with Amazon RDS, serverless)
• Determining cost-effective AWS database types (for example, time series format, columnar format)
• Migrating database schemas and data to different locations and/or different database engines

Module 4: Design cost-optimized network architectures.

• AWS cost management service features (for example, cost allocation tags, multi-account billing)
• AWS cost management tools with appropriate use cases (for example, Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
• Load balancing concepts (for example, Application Load Balancer)
• NAT gateways (for example, NAT instance costs compared with NAT gateway costs)
• Network connectivity (for example, private lines, dedicated lines, VPNs)
• Network routing, topology, and peering (for example, AWS Transit Gateway, VPC peering)
• Network services with appropriate use cases (for example, DNS) LAB:
• Configuring appropriate NAT gateway types for a network (for example, a single shared NAT gateway compared with NAT gateways for each Availability Zone)
• Configuring appropriate network connections (for example, Direct Connect compared with VPN compared with internet)
• Configuring appropriate network routes to minimize network transfer costs (for example, Region to Region, Availability Zone to Availability Zone, private to public, Global Accelerator, VPC endpoints)
• Determining strategic needs for content delivery networks (CDNs) and edge caching
• Reviewing existing workloads for network optimizations
• Selecting an appropriate throttling strategy