MLA-C01 ML Model Development Practice Question
An ML engineer is fine-tuning a foundation model using RLHF on SageMaker. Which THREE components are essential for this workflow? (Select THREE.)
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
✓
A reward model trained on the preference data
RLHF requires a preference dataset for human feedback, a reward model trained on that data, and the PPO algorithm to update the foundation model. The PEFT technique (like LoRA) is often used to make fine-tuning efficient, but it is not strictly essential for RLHF; however, it is commonly used. The base foundation model is required. A validation dataset is needed but not specific to RLHF.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A reward model trained on the preference data
Why this is correct
The reward model scores outputs for the PPO algorithm.
- ✗
A large validation dataset for final evaluation
Why it's wrong here
Validation is important but not a core component of the RLHF workflow.
- ✓
The PPO (Proximal Policy Optimization) algorithm for model updates
Why this is correct
PPO is the standard algorithm used in RLHF to update the policy.
- ✓
A preference dataset with human rankings
Why this is correct
RLHF requires a dataset of human preferences to train the reward model.
- ✗
A PEFT technique like LoRA
Why it's wrong here
PEFT is common but not strictly required; full fine-tuning is also possible.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.