Explain how organizations unlock business value from data and differentiate between structured, semistructured, and unstructured data types.

Select the appropriate Google Cloud data management solution to meet specific organizational workloads.

Explain how to derive actionable business intelligence and evaluate the impact of real-time streaming analytics.

Explore the fundamental concepts of artificial intelligence, machine learning, generative AI, and agentic AI.

Identify the specific business problems machine learning can solve, along with the importance of high quality data and responsible AI practices.

Describe the four distinct paths to building machine learning models on Google Cloud, and the AI Hypercomputer architecture that supports them.

Examine the strategic tradeoffs around speed, differentiation, expertise, and effort required to choose the right AI solution for an organization.

Define cloud migration terminology and explain the value of shifting from traditional hardware to cloud computing models.

Explore the differences between virtual machine (VM), container, and serverless computing architectures.

Examine strategies for managing data and compute workloads across hybrid and multicloud distributed environments.

Compare traditional and modern cloud app development, and identify tools for rehosting legacy apps.

Describe the fundamental security concepts used to manage digital identities and access controls within your cloud environment.

Explore Google Cloud’s defense-in-depth approach and AI tools, such as Model Armor, to help secure your infrastructure.

Navigate trust principles and sovereignty controls to ensure your data stays compliant with global and regional regulations.

Explain how cloud costs are managed by describing the CapEx to OpEx shift and the use of the resource hierarchy and Google Cloud cost tools.

Describe cloud reliability by identifying the four golden signals and the relationship between DevOps, SRE, and service level performance targets.