AWS Cloud Architect
The AWS Cloud Architect course offers advanced training on designing and deploying scalable, secure, and cost-effective solutions using Amazon Web Services (AWS). Participants learn to architect complex cloud infrastructures, manage resources, and integrate various AWS services to meet business requirements. The course covers key topics such as high availability, disaster recovery, security, and cost optimization, equipping professionals with the skills needed to design effective cloud architectures and prepare for AWS Certified Solutions Architect exams.
Training Calender
| Start Date | End Date | Start-End Time | Batch Type | Training Mode | Batch Status | Start Learning |
|---|---|---|---|---|---|---|
| 27th Sep 2024 | 25th Nov 2024 | 09:00 - 13:00 IST | Weekend | Online | (Open) | Enroll Now |
Course Syllabus
Domain 1: (Design Secure Architectures)
Task Statement 1.1: Design secure access to AWS resources.
Knowledge of:
• Access controls and management across multiple accounts
• AWS federated access and identity services (for example, AWS Identity and
Access Management [IAM], AWS IAM Identity Center [AWS Single Sign-On])
• 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
Skills in:
• 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
Task Statement 1.2: Design secure workloads and applications.
Knowledge of:
• 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)
Skills in:
• 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, IAM Identity Center, AWS Secrets Manager)
• Securing external network connections to and from the AWS Cloud (for
example, VPN, AWS Direct Connect)
Task Statement 1.3: Determine appropriate data security controls.
Knowledge of:
• Data access and governance
• Data recovery
• Data retention and classification
• Encryption and appropriate key management
Skills in:
• 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
Domain 2: (Design Resilient Architectures )
Task Statement 2.1: Design scalable and loosely coupled architectures.
Knowledge of:
• 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)
Skills in:
• 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
Task Statement 2.2: Design highly available and/or fault-tolerant architectures.
Knowledge of:
• 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)
Skills in:
• 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
Domain 3: (Design High-Performing Architectures )
Task Statement 3.1: Determine high-performing and/or scalable storage solutions.
Knowledge of:
• 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)
Skills in:
• Determining storage services and configurations that meet performance
demands
• Determining storage services that can scale to accommodate future needs
Task Statement 3.2: Design high-performing and elastic compute solutions.
Knowledge of:
• 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)
Skills in:
• 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
Task Statement 3.3: Determine high-performing database solutions.
Knowledge of:
• 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 writeintensive)
• 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)
Skills in:
• 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
Task Statement 3.4: Determine high-performing and/or scalable network
architectures.
Knowledge of:
• 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)
Skills in:
• 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
Task Statement 3.5: Determine high-performing data ingestion and transformation
solutions.
Knowledge of:
• 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)
Domain 4: (Design Cost-Optimized Architectures)
Task Statement 4.1: 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)
Skills in:
• 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
Task Statement 4.2: Design cost-optimized compute solutions.
Knowledge of:
• 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)
Skills in:
• 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
Task Statement 4.3: Design cost-optimized database solutions.
Knowledge of:
• 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 nonrelational, Aurora, DynamoDB)
Skills in:
• 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
Task Statement 4.4: Design cost-optimized network architectures.
Knowledge of:
• 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)
Skills in:
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
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