Generative AI Leader Fundamentals of Generative AI Practice Question
A data scientist is selecting a base model for generating Python code. Which TWO factors are most important to consider?
⚠ Common exam trap
Google Cloud often tests the misconception that larger parameter counts or broader language support are more important than licensing and benchmark performance, leading candidates to overlook the legal and functional constraints of deploying a code generation model in a business context.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Model's license (proprietary vs open-source).
The model's license determines whether the generated code can be used in commercial products without violating copyright or requiring attribution. Proprietary models may impose restrictions on output usage, while open-source models (e.g., CodeLlama, StarCoder) offer more flexibility for enterprise deployment. This is critical for compliance and intellectual property management in production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Model's license (proprietary vs open-source).
Why this is correct
License determines usage rights and compliance.
- ✓
Model's performance on coding benchmarks like HumanEval.
Why this is correct
Benchmarks provide objective evaluation of code generation ability.
- ✗
Model's support for multiple programming languages.
Why it's wrong here
While nice to have, the primary need is Python-only; multilingual support is not essential.
- ✗
Model's training data recency.
Why it's wrong here
Recency may affect knowledge of latest libraries but is less critical than license and benchmark scores.
- ✗
Model's parameter count (size).
Why it's wrong here
Size correlates with capability but is not as critical as license and benchmark performance.
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