mediumMultiple Select
Generative AI Leader Practice Question: A data scientist is documenting a new dataset for…
A data scientist is documenting a new dataset for a generative AI project. According to the Responsible AI toolkit, which TWO elements should they include in a Datasheet for Datasets?
⚠ Common exam trap
The Generative AI Leader exam often tests the distinction between dataset documentation (Datasheet for Datasets) and model documentation (Model Cards), so candidates mistakenly include model-specific details like architecture or hyperparameters instead of dataset-focused elements.
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
✓
The demographic composition of the data subjects
Option B is correct because a Datasheet for Datasets, as promoted by the Responsible AI toolkit, should document the demographic composition of data subjects to surface potential representation gaps, bias risks, and fairness considerations for the generative AI project. Option D is correct because datasheets must state the intended use cases and limitations so downstream consumers understand appropriate applications and avoid misuse or overclaiming model capabilities. Options A and C are incorrect because model architecture and hyperparameters describe model training or inference configuration, not the dataset documentation itself. Option E is incorrect because acquisition cost is a procurement or business metric, not a required element of a Datasheet for Datasets under the Responsible AI toolkit.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model architecture used to collect the data
Why it's wrong here
A Datasheet documents the dataset itself: motivation, composition, collection process, preprocessing, uses and distribution. Model architecture belongs to a Model Card, which describes the trained model. Architecture would be the correct element when documenting a model rather than the underlying data.
- ✓
The demographic composition of the data subjects
Why this is correct
Datasheets for Datasets require documenting dataset composition, including demographic characteristics of data subjects, to expose potential representation gaps and bias. This transparency element satisfies the toolkit's documentation requirement, enabling downstream assessment of whether the dataset fairly represents the populations the generative AI system will serve.
- ✗
The hyperparameters of the model that will process the data
Why it's wrong here
Hyperparameters describe how a model is trained, not the dataset being documented, so they belong in a Model Card. A Datasheet covers motivation, composition, collection, preprocessing and intended uses. Hyperparameters would be the right entry when recording training configuration for reproducibility of a model.
- ✓
The intended use cases and limitations
Why this is correct
Datasheets for Datasets must state intended use cases and explicit limitations, clarifying where the dataset is appropriate and where it is not. This satisfies the toolkit's transparency requirement by preventing misuse and setting stakeholder expectations about the generative AI system's valid scope.
- ✗
The cost of acquiring the dataset
Why it's wrong here
Cost of acquisition is a procurement or budgeting detail, not a Responsible AI Datasheet element; Datasheets document motivation, composition, collection process, preprocessing, uses, distribution and maintenance. It is tempting because cost data belongs in project financial records, and would be relevant when justifying dataset spend to finance stakeholders.
Go deeper
Related to this question
About these practice questions
One of 1,008 original Generative AI Leader practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.