Google Cloud Certified - Cloud Digital Leader Flashcards

Business and technical professionals who need to articulate the capabilities of Google Cloud products and services and how they benefit organizations, without requiring hands-on engineering skills.

Six sections: Digital Transformation, Data Transformation, Artificial Intelligence, Modernize Infrastructure and Applications, Trust and Security, and Scaling with Google Cloud Operations.

The process of using cloud technology to fundamentally change how an organization operates and delivers value to customers, often shifting from capital-heavy on-premises IT to flexible, pay-as-you-go cloud services.

CapEx (capital expenditure) is upfront spending on owned infrastructure like servers; OpEx (operational expenditure) is ongoing pay-as-you-go spending. Cloud computing shifts IT costs from CapEx to OpEx.

A data warehouse is a centralized repository optimized for analyzing large volumes of structured and semi-structured data, typically used for business intelligence and reporting rather than transactional workloads.

A data lake stores raw data in its native format (structured, semi-structured, or unstructured) at any scale, whereas a data warehouse stores processed, structured data optimized for query and analysis.

Google Cloud's serverless, highly scalable enterprise data warehouse designed for fast SQL-based analytics over large datasets, with built-in machine learning capabilities.

ETL (Extract, Transform, Load) transforms data before loading it into the target system; ELT (Extract, Load, Transform) loads raw data first and transforms it within the target system, which is common with modern cloud data warehouses.

A subset of artificial intelligence in which systems learn patterns from data to make predictions or decisions without being explicitly programmed for every scenario.

A ready-to-use machine learning model exposed via an API (such as Vision AI or Natural Language AI) that lets developers add AI capabilities without training their own models.

Google Cloud's unified machine learning platform for building, training, deploying, and managing custom and generative AI models across the full ML lifecycle.

AI models capable of creating new content—such as text, images, audio, or code—based on patterns learned from training data, often in response to a prompt.

A type of generative AI model trained on massive amounts of text data that can understand and generate human-like language for tasks like summarization, translation, and conversation.

A cloud computing model where the provider manages physical infrastructure while the customer manages operating systems, middleware, and applications; Compute Engine is a Google Cloud example.

A cloud computing model where the provider manages the underlying infrastructure and runtime, letting customers focus on deploying and managing applications; App Engine is a Google Cloud example.

A cloud computing model where the provider fully manages a complete application that customers access over the internet, such as Google Workspace.

Google Cloud's Infrastructure as a Service offering that provides configurable, scalable virtual machines running in Google's data centers.

A managed service for running containerized applications using Kubernetes, automating deployment, scaling, and operations of container clusters on Google Cloud.

A fully managed serverless platform for running stateless containers that automatically scales up or down, including to zero, based on incoming traffic.

A cloud execution model where the provider automatically manages infrastructure provisioning and scaling, so developers only write and deploy code without managing servers.

The practice of updating legacy applications—often through rehosting, replatforming, or refactoring—to run more efficiently and flexibly in the cloud, such as by containerizing them or adopting microservices.

A framework defining which security tasks the cloud provider handles (e.g., physical infrastructure, hypervisor) versus which the customer handles (e.g., data classification, access controls), with the exact split varying by service model.

A Google Cloud service that lets administrators control who (identity) has what level of access (roles/permissions) to specific cloud resources, following the principle of least privilege.

A security best practice granting users and services only the minimum permissions necessary to perform their tasks, reducing the potential impact of compromised credentials.

A logically isolated, private network within Google Cloud where an organization can deploy and control resources such as VM instances, firewall rules, and subnets.

A discipline pioneered at Google that applies software engineering practices to IT operations, focusing on reliability, automation, and using metrics like SLIs, SLOs, and error budgets to manage services.

SLA (Service Level Agreement) is a formal commitment to customers about service performance; SLO (Service Level Objective) is an internal target for reliability; SLI (Service Level Indicator) is the actual measured metric used to evaluate performance against the SLO.

Google Cloud's set of tools for monitoring, logging, and diagnosing the health, performance, and availability of applications and infrastructure running on Google Cloud.

An evaluation comparing all costs of running infrastructure on-premises versus in the cloud, including hardware, maintenance, staffing, and operational expenses, to justify migration decisions.

A common set of migration approaches—rehost, replatform, refactor, repurchase, retire, and retain—describing different levels of change applied when moving workloads to the cloud.