Among the certifications offered by Google are six specifically related to cloud-based solutions and the Google Cloud Platform — the suite of cloud computing services offered by Google. Each of the six certifications requires passing a single corresponding exam and there are no prerequisites associated with any of them. Once gained, the certification is then good for two years from the date of passing: those who do not recertify within the two years lose their certification.

If you fail an exam, you can take it over, but there is a bit of a penalty involved. Failing an exam the first time triggers a 14-day waiting period before you can attempt it again. Failing an exam a second time triggers a 60-day waiting period, and failing it a third means you have to wait for one year before trying again.

What follows is an overview of each of the six cloud-related certifications including what they cover. More information — and the most current updates — can be found on the Google Cloud certification site. We'll cover the first three credentials today, and then come back on Thursday to wrap up with the final three.

Google Cloud Certified — Associate Cloud Engineer

The Associate Cloud Engineer certification is geared toward those who have mastered the command-line interface and can deploy apps, monitor operations, and maintain cloud projects. The exam is multiple choice/multiple response, 2 hours in length, and priced at $125. This is the only associate-level exam/certification on the technology, and it is a good starting point for any of the other (professional-level) certifications.

Here's a breakdown of what's covered in this certification:

SECTION 1 — Setting Up a Cloud Solution Environment
1.1 Setting up cloud projects and accounts. Activities include:

• ‍‍Creating projects
• Assigning users to pre-defined IAM roles within a project
• Linking users to G Suite identities
• Enabling APIs within projects
• Provisioning one or more Stackdriver accounts

‍1.2 Managing billing configuration. Activities include:

• ‍‍Creating one or more billing accounts
• Linking projects to a billing account
• Establishing billing budgets and alters
• Setting up billing exports to estimate daily/monthly charges

‍1.3 Installing and configuring the command line interface (CLI), specifically the Cloud SDK

SECTION 2 — Planning and Configuring a Cloud Solution
2.1 Planning and estimating GCP product use using the Pricing Calculator
2.2 Planning and configuring compute resources. Considerations include:

• ‍‍Selecting appropriate compute choices for a given workload
• Using preemptible VMs and custom machine types as appropriate

‍2.3 Planning and configuring data storage options. Considerations include:

• ‍‍Products choice
• Choosing storage options

‍2.4 Planning and configuring network resources. Tasks include:

• ‍‍Differentiating load balancing options
• Identifying resource locations in a network for availability
• Configuring Cloud DNS

‍SECTION 3 — Deploying and Implementing a Cloud Solution
3.1 Deploying and implementing Compute Engine resources. Tasks include:

• ‍‍Launching a compute instance using Cloud Console and Cloud SDK (gcloud)
• Creating an autoscaled managed instance group using an instance template
• Generating/uploading a custom SSH key for instances
• Configuring a VM for Stackdriver monitoring and logging
• Assessing compute quotas and requesting increases
• Installing the Stackdriver Agent for monitoring and logging

‍3.2 Deploying and implementing Kubernetes Engine resources. Tasks include:

• ‍‍Deploying a Kubernetes Engine cluster
• Deploying a container application to Kubernetes Engine using pods
• Configuring Kubernetes Engine application monitoring and logging

‍3.3 Deploying and implementing App Engine and Cloud Functions resources. Tasks include:

• ‍‍Deploying an application to App Engine
• Deploying a Cloud Function that receives Google Cloud events

‍3.4 Deploying and implementing data solutions. Tasks include:

• ‍‍Initializing data systems with products
• Loading data

‍3.5 Deploying and implementing networking resources. Tasks include:

• ‍‍Creating a VPC with subnets
• Launching a Compute Engine instance with custom network configuration
• Creating ingress and egress firewall rules for a VPC
• Creating a VPN between a Google VPC and an external network using Cloud VPN
• Creating a load balancer to distribute application network traffic to an application

‍3.6 Deploying a Solution using Cloud Launcher. Tasks include:

• ‍‍Browsing Cloud Launcher catalog and viewing solution details
• Deploying a Cloud Launcher marketplace solution

‍3.7 Deploying an Application using Deployment Manager. Tasks include:

• ‍‍Developing Deployment Manager templates to automate deployment of an application
• Launching a Deployment Manager template to provision GCP resources and configure an application automatically

‍SECTION 4 — Ensuring Successful Operation of a Cloud Solution
4.1 Managing Compute Engine resources. Tasks include:

• ‍‍Managing a single VM instance
• SSH/RDP to the instance
• Attaching a GPU to a new instance and installing CUDA libraries
• Viewing current running VM Inventory (instance IDs, details)
• Working with snapshots
• Working with Images
• Working with Instance Groups
• Working with management interfaces

‍4.2 Managing Kubernetes Engine resources. Tasks include:

• ‍‍Viewing current running cluster inventory (nodes, pods, services)
• Browsing the container image repository and viewing container image details
• Working with nodes
• Working with pods
• Working with services
• Working with management interfaces

‍4.3 Managing App Engine resources. Tasks include:

• ‍‍Adjusting application traffic splitting parameters
• Setting scaling parameters for autoscaling instances
• Working with management interfaces

‍4.4 Managing data solutions. Tasks include:

• ‍‍Executing queries to retrieve data from data instances
• Estimating costs of a BigQuery query
• Backing up and restoring data instances
• Reviewing job status in Cloud Dataproc or BigQuery
• Moving objects between Cloud Storage buckets
• Converting Cloud Storage buckets between storage classes
• Setting object lifecycle management policies for Cloud Storage buckets
• Working with management interfaces

‍4.5 Managing networking resources. Tasks include:

• ‍‍Adding a subnet to an existing VPC
• Expanding a CIDR block subnet to have more IP addresses
• Reserving static external or internal IP addresses
• Working with management interfaces

‍4.6 Monitoring and logging. Tasks include:

• ‍‍Creating Stackdriver alerts based on resource metrics
• Creating Stackdriver custom metrics
• Configuring log sinks to export logs to external systems
• Viewing and filtering logs in Stackdriver
• Viewing specific log message details in Stackdriver
• Using cloud diagnostics to research an application issue
• Viewing Google Cloud Platform status
• Working with management interfaces

‍SECTION 5 — Configuring Access and Security
5.1 Managing Identity and Access Management (IAM). Tasks include:

• ‍‍Viewing account IAM assignments
• Assigning IAM roles to accounts or Google Groups
• Defining custom IAM roles

‍5.2 Managing service accounts. Tasks include:

• ‍‍Managing service accounts with limited scopes
• Assigning a service account to VM instances
• Granting access to a service account in another project

‍5.3 Viewing audit logs for project and managed services

Google Cloud Certified — Professional Data Engineer

According to Google, a professional data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. The exam is multiple choice/multiple response, 2 hours in length, and priced at $200 (USD).

The exam was recently updated to remove fictitious case studies that had been previously tested on. Here's a breakdown of what's covered in this certification:

SECTION 1 — Designing Data Processing Systems
1.1 Select the appropriate storage technologies. Considerations include:

• ‍‍Mapping storage systems to business requirements
• Data modeling
• Tradeoffs involving latency, throughput, transactions
• Distributed systems
• Schema design

‍1.2 Designing data pipelines. Considerations include:

• ‍‍Data publishing and visualization
• Batch and streaming data
• Online (interactive) vs. batch predictions
• Job automation and orchestration

‍1.3 Designing a data processing solution. Considerations include:

• ‍‍Choice of infrastructure
• System availability and fault tolerance
• Use of distributed systems
• Capacity planning
• Hybrid cloud and edge computing
• Architecture options
• At least once, in-order, and exactly once, etc., event processing

‍1.4 Migrating data warehousing and data processing. Considerations include:

• ‍‍Awareness of current state and how to migrate a design to a future state
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Validating a migration

‍SECTION 2 — Building and Operationalizing Data Processing Systems
2.1 Building and operationalizing storage systems. Considerations include:

• ‍‍Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Storage costs and performance
• Lifecycle management of data

‍2.2 Building and operationalizing pipelines. Considerations include:

• ‍‍Data cleansing
• Batch and streaming
• Transformation
• Data acquisition and import
• Integrating with new data sources

‍2.3 Building and operationalizing processing infrastructure. Considerations include:

• ‍‍Provisioning resources
• Monitoring pipelines
• Adjusting pipelines
• Testing and quality control

‍SECTION 3 — Operationalizing Machine Learning Models
3.1 Leveraging pre-built ML models as a service. Considerations include:

• ‍‍ML APIs
• Customizing ML APIs
• Conversational experiences

‍3.2 Deploying an ML pipeline. Considerations include:

• ‍‍Ingesting appropriate data
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Continuous evaluation

‍3.3 Choosing the appropriate training and serving infrastructure. Considerations include:

• ‍‍Distributed vs. single machine
• Use of edge compute
• Hardware accelerators

‍3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include:

• ‍‍Machine Learning terminology
• Impact of dependencies of machine learning models
• Common sources of error

‍SECTION 4 — Ensuring Solution Quality
4.1 Designing for security and compliance. Considerations include:

• ‍‍Identity and access management
• Data security (encryption, key management)
• Ensuring privacy
• Legal compliance

‍4.2 Ensuring scalability and efficiency. Considerations include:

• ‍‍Building and running test suites
• Pipeline monitoring
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Resizing and autoscaling resources

‍4.3 Ensuring reliability and fidelity. Considerations include:

• ‍‍Performing data preparation and quality control
• Verification and monitoring
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Choosing between ACID, idempotent, eventually consistent requirements

‍4.4 Ensuring flexibility and portability. Considerations include:

• ‍‍Mapping to current and future business requirements
• Designing for data and application portability
• Data staging, cataloging and discovery

‍

‍Google Cloud Certified — Professional Cloud Architect

The professional cloud architect certification is geared toward those who can design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives. The exam is multiple choice/multiple response, 2 hours in length, and priced at $200.

A number of the questions asked will refer to case studies involving fictional companies (Dress4Win, Mountkirk Games, and TerramEarth) and you can find the case studies ahead of time on the Google certification site.  Here's a breakdown of what's covered in this certification:

SECTION 1 — Designing and Planning a Cloud Solution Architecture
1.1 Designing a solution infrastructure that meets business requirements. Considerations include:

• ‍‍Business use cases and product strategy
• Cost optimization0
• Supporting the application design
• Integration
• Movement of data
• Tradeoffs
• Build, buy or modify
• Success measurements
• Compliance and observability

‍1.2 Designing a solution infrastructure that meets technical requirements. Considerations include:

• ‍‍High availability and failover design
• Elasticity of cloud resources
• Scalability to meet growth requirements

‍1.3 Designing network, storage, and compute resources. Considerations include:

• I‍ntegration with on premises/multi-cloud environments
• Cloud native networking (VPC, peering, firewalls, container networking)
• Identification of data processing pipeline
• Matching data characteristics to storage systems
• Data flow diagrams
• Storage system structure
• Mapping compute needs to platform products

‍1.4 Creating a migration plan. Considerations include:

• ‍‍Integrating solution with existing systems
• Migrating systems and data to support the solution
• Licensing mapping
• Network and management planning
• Testing and proof-of-concept

‍1.5 Envisioning future solution improvements. Considerations include:

• ‍‍Cloud and technology improvements
• Business needs evolution
• Evangelism and advocacy

‍SECTION 2 — Mapping and Provisioning Solution Infrastructure
2.1 Configuring network topologies. Considerations include:

• ‍‍Extending to on-premise (hybrid networking)
• Extending to a multi-cloud environment which may include GCP to GCP communication
• Security
• Data protection

‍2.2 Configuring individual storage systems. Considerations include:

• ‍‍Data storage allocation
• Data processing/compute provisioning
• Security and access management
• Network configuration for data transfer and latency
• Data retention and data lifecycle management
• Data growth management

‍2.3 Configuring compute systems. Considerations include:

• ‍‍Compute system provisioning
• Compute volatility configuration (preemptible vs. standard)
• Network configuration for compute nodes
• Infrastructure provisioning technology configuration
• Container orchestration

‍SECTION 3 — Designing for Security and Compliance
3.1 Designing for security. Considerations include:

• ‍‍Identity and Access Management (IAM)
• Resource hierarchy (organizations, folders, projects)
• Data security (key management, encryption)
• Penetration testing
• Separation of Duties (SoD)
• Security controls
• Managing customer-supplied encryption keys with Cloud KMS

‍3.2 Designing for legal compliance. Considerations include:

• ‍‍Legislation
• Audits (including logs)
• Certification

‍SECTION 4 — Analyzing and Optimizing Technical and Business Processes
4.1 Analyzing and defining technical processes. Considerations include:

• ‍‍Software Development Lifecycle Plan (SDLC)
• Continuous integration/continuous deployment
• Troubleshooting/post mortem analysis culture
• Testing and validation
• IT enterprise process
• Business continuity and disaster recovery

‍4.2 Analyzing and defining business processes. Considerations include:

• ‍‍Stakeholder management
• Change management
• Team assessment/skills readiness
• Decision making process
• Customer success management
• Cost optimization/resource optimization (Capex/Opex)

‍4.3 Developing procedures to test resilience of solution in production

SECTION 5 — Managing Implementation
5.1 Advising deployment/operation team(s) to ensure successful deployment of the solution. Considerations include:

• ‍‍Application development
• API best practices
• Testing frameworks (load/unit/integration)
• Data and system migration tooling

‍5.2 Interacting with Google Cloud using GCP SDK (gcloud, gsutil, and bq). Considerations include:

• ‍‍Local installation
• Google Cloud Shell

‍SECTION 6 — Ensuring solution and operations reliability
6.1 Monitoring/Logging/Alerting solution
6.2 Deployment and release management
6.3 Supporting operational troubleshooting
6.4 Evaluating quality control measures