Section 1: Designing highly scalable, available,
and reliable cloud-native applications
1.1 Designing high-performing applications and
APIs. Considerations include:
â—Ź Microservices architecture
â—Ź Choosing the appropriate
platform based on the use case and requirements
(e.g., IaaS [infrastructure as a service], CaaS
[container as a service], PaaS [platform as a
service], FaaS [function as a service])
â—Ź Application modernization
(e.g., containerization)
â—Ź Understanding how Google
Cloud services are geographically distributed (e.g.,
latency, regional services, zonal services)
â—Ź User session management
â—Ź Caching solutions
â—Ź HTTP REST versus gRPC (Google
Remote Procedure Call)
â—Ź Incorporating Service Control
capabilities offered by API services (e.g. Apigee)
â—Ź Loosely coupled asynchronous
applications (e.g., Apache Kafka, Pub/Sub, Eventarc)
â—Ź Instrumenting code to produce
metrics, logs, and traces
â—Ź Cost optimization and
resource optimization
â—Ź Graceful handling of errors,
disasters, and scaling events
1.2 Designing secure applications. Considerations
include:
â—Ź Implementing data lifecycle
and residency for applicable regulatory requirements
â—Ź Security mechanisms that
identify vulnerabilities and protect services and
resources (e.g., Identity-Aware Proxy [IAP], Web
Security Scanner)
â—Ź Security mechanisms that
secure/scan application binaries, dependencies, and
manifests (e.g., Container Analysis)
â—Ź Storing, accessing, and
rotating application secrets and encryption keys
(e.g., Secret Manager, Cloud Key Management Service)
â—Ź Authenticating to Google
Cloud services (e.g., application default
credentials, JSON Web Token [JWT], OAuth 2.0)
â—Ź End-user account management
and authentication by using Identity Platform
â—Ź Identity and Access
Management (IAM) roles for users, groups, and
service accounts
â—Ź Securing service-to-service
communications (e.g., service mesh, Kubernetes
Network Policies, Kubernetes namespaces)
â—Ź Running services with keyless
and least privileged access (e.g., Workload
Identity, Workload identity federation)
â—Ź Certificate-based
authentication (e.g., SSL, mTLS)
â—Ź Supply-chain Levels for
Software Artifacts (SLSA)
1.3 Choosing storage options for application data.
Considerations include:
â—Ź Time-limited access to
objects
â—Ź Data retention requirements
â—Ź Structured versus
unstructured data (e.g., SQL versus NoSQL)
â—Ź Strong versus eventual
consistency
â—Ź Data volume
â—Ź Data access patterns
â—Ź Online transaction processing
(OLTP) versus data warehousing
Section 2: Building and testing applications
2.1 Setting up your local development environment.
Considerations include:
â—Ź Emulating Google Cloud
services for local application development
â—Ź Using the Google Cloud
console, Google Cloud SDK, Cloud Shell, and Cloud
Workstations
â—Ź Using developer tooling
(e.g., common IDEs, Cloud Code, Skaffold)
â—Ź Authenticating to Google
Cloud services (e.g., Cloud SQL Auth proxy, AlloyDB
Auth proxy)
2.2 Building. Considerations include:
â—Ź Source control management
â—Ź Creating secure container
images from code
â—Ź Developing a continuous
integration pipeline by using services (e.g., Cloud
Build, Artifact Registry) that construct deployment
artifacts
â—Ź Code and test build
optimization
2.3 Testing. Considerations include:
â—Ź Unit testing
â—Ź Integration testing including
the use of emulators
â—Ź Performance testing
â—Ź Load testing
â—Ź Failure testing/chaos
engineering
Section 3: Deploying applications
3.1 Adopting appropriate feature rollout
strategies. Considerations include:
â—Ź A/B testing
â—Ź Feature flags
â—Ź Backward compatibility
â—Ź Versioning APIs (e.g.,
Apigee)
3.2 Deploying applications to a serverless
computing environment. Considerations include:
â—Ź Deploying applications from
source code
â—Ź Using triggers to invoke
functions
â—Ź Configuring event receivers
(e.g., Eventarc, Pub/Sub)
â—Ź Exposing and securing
application APIs (e.g., Apigee)
3.3 Deploying applications and services to Google
Kubernetes Engine (GKE). Considerations include:
â—Ź Deploying a containerized
application to GKE
â—Ź Integrating Kubernetes
role-based access control (RBAC) with IAM
â—Ź Defining workload
specifications (e.g., resource requirements)
â—Ź Building a container image by
using Cloud Build
Section 4: Integrating an application with Google
Cloud services
4.1 Integrating an application with data and
storage services. Considerations include:
â—Ź Managing connections to
datastores (e.g., Cloud SQL, Firestore, Bigtable,
Cloud Storage)
â—Ź Reading/writing data to or
from various datastores
â—Ź Writing an application that
publishes or consumes data asynchronously (e.g.,
from Pub/Sub or streaming data sources)
â—Ź Orchestrate application
services with Workflows, Eventarc, Cloud Tasks, and
Cloud Scheduler
4.2 Integrating an application with Google Cloud
APIs. Considerations include:
â—Ź Enabling Google Cloud
services
â—Ź Making API calls by using
supported options (e.g., Cloud Client Library, REST
API or gRPC, API Explorer) taking into
consideration:
â—‹ Batching
requests
â—‹
Restricting return data
â—‹
Paginating results
â—‹ Caching
results
â—‹ Error
handling (e.g., exponential backoff)
â—Ź Using service accounts to
make Cloud API calls
â—Ź Integrating with Google
Cloud’s operations suite
Note: **The topics said above are only the short blueprint of the syllabus. On the off chance that you feel that we have missed any subject, you can simply come to us and learn it, or simply call us to affirm
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