Foundation Model Landscape and Limitations
Leaders should understand foundation models, modalities, model selection, hallucination, bias, latency, cost, context limits, and responsible constraints.
How to study for Google Cloud Generative AI Leader
Treat each item as a leadership decision: define business value, match Google Cloud capabilities, improve output quality, then govern rollout responsibly.
Core concepts
Concept 1
Foundation models are broadly trained models that can be adapted to tasks through prompts, grounding, tuning, tools, or application logic.
Exam cue: Choose the smallest capable model when cost, latency, or scale matters.
Concept 2
Model selection balances modality, capability, latency, cost, privacy, regional availability, output quality, and governance needs.
Exam cue: Use grounding and evaluation when factuality matters.
Concept 3
Model limitations include hallucination, bias, context limits, outdated knowledge, non-determinism, and security risks.
Exam cue: Treat model output as probabilistic unless validated by controls or source evidence.
Risk pitfalls and guardrails
Assuming a larger model always produces better business value.
Guardrail: Avoid choosing a model before proving business value, data readiness, evaluation criteria, and responsible AI controls.
Using model confidence wording as proof of correctness.
Guardrail: Avoid choosing a model before proving business value, data readiness, evaluation criteria, and responsible AI controls.
Ignoring cost and latency when moving from prototype to production.
Guardrail: Avoid choosing a model before proving business value, data readiness, evaluation criteria, and responsible AI controls.
Memory anchors
Foundation Model
A foundation model is a broadly trained model adapted to many tasks through prompts, grounding, tools, or tuning.
Modality
Modality is the input or output type, such as text, image, audio, video, code, or structured data.
Context Limit
A context limit is the maximum amount of instructions and source material a model can consider in one request.
Hallucination
Hallucination is an unsupported or incorrect output that appears plausible.
Model Fit
Model fit weighs capability, cost, latency, modality, data rules, risk, and operational needs.
Non-Determinism
Non-determinism means the same prompt can produce varied outputs unless settings and controls constrain variation.
Bias Risk
Bias risk is the chance that outputs reflect unfair, incomplete, or harmful patterns in data or design.
Latency Tradeoff
Latency tradeoff balances output quality and model complexity against user response-time expectations.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Which layer of the GenAI landscape provides accelerators, storage, networking, and compute capacity?
Which layer provides broadly learned capabilities such as language or image generation?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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