Advanced AI, Security, and Compliance Management on Google Cloud Literacy (Beginner Level)

Topics covered

• analyze BigQuery ML, pre-trained application programming interfaces (APIs), AutoML and custom trained models
• analyze Google Cloud support for key management
• cite tenets of responsible AI
• contrast authentication, authorization, and audit
• contrast AutoML and custom training
• define and apply Google Cloud resource hierarchies
• define basic, predefined, and custom roles in Cloud IAM
• define basic terms related to site reliability
• define generative AI and differentiate between generative and discriminative models
• describe and analyze observability tools such as monitoring and logging
• describe cost management tools on Google Cloud, including the Cloud FinOps operating model
• describe the types of principals in Cloud Identity and Access Management (IAM)
• differentiate between on-premises security and cloud security
• draw the workflow of generative AI use cases on Google Cloud
• enumerate the attributes of good data
• estimate cloud costs with Google Cloud Billing Calculator
• evaluate different ML training options on the Google Cloud using multiple criteria
• identify ways of controlling cloud consumption
• list cloud financial governance best practices
• list security features in Google data centers
• navigate Google Cloud compliance resources and use the search and filter functionality to find regulations and standards
• outline Google's trust principles and data sovereignty considerations
• outline security measures to protect cloud resources
• outline the features of TensorFlow and the roles of CPUs, graphics processing units (GPUs), and tensor processing units (TPUs)
• provide an overview of financial governance on the cloud, including challenges and cost management
• provide an overview of the DevOps paradigm
• provide an overview of the place of Vertex AI among Google Cloud offerings
• provide an overview of the workings of Google Cloud customer care and support