Question 847 of 997

Generative AI Concepts and Technologies →easyMultiple ChoiceObjective-mapped

Generative AI Leader Generative AI Concepts and Technologies Practice Question

This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

What is the key advantage of using adapter-based fine-tuning methods like LoRA compared to full fine-tuning of a large language model?

A
LoRA significantly reduces the number of trainable parameters, making fine-tuning more memory-efficient
LoRA updates only low‑rank matrices, drastically cutting trainable parameters and memory usage while maintaining performance.
B
LoRA is faster at inference time compared to the fully fine-tuned model
Why wrong: LoRA adapters add a small overhead; inference speed is comparable, not faster.
C
LoRA eliminates the need for a base model
Why wrong: LoRA is applied on top of a pre‑trained base model; it cannot work without one.
D
LoRA enables training on a larger dataset than full fine-tuning
Why wrong: LoRA does not inherently allow larger datasets; it reduces per‑example memory but the dataset size is independent.

Full breakdown with real-world 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

✓

LoRA significantly reduces the number of trainable parameters, making fine-tuning more memory-efficient

LoRA (Low-Rank Adaptation) injects trainable low-rank matrices into the transformer layers while keeping the original model weights frozen. This drastically reduces the number of trainable parameters (often by 10,000x), which lowers GPU memory requirements for storing optimizer states and gradients during training, making fine-tuning feasible on consumer hardware.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
LoRA significantly reduces the number of trainable parameters, making fine-tuning more memory-efficient
Why this is correct
LoRA updates only low‑rank matrices, drastically cutting trainable parameters and memory usage while maintaining performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
LoRA is faster at inference time compared to the fully fine-tuned model
Why it's wrong here
LoRA adapters add a small overhead; inference speed is comparable, not faster.
✗
LoRA eliminates the need for a base model
Why it's wrong here
LoRA is applied on top of a pre‑trained base model; it cannot work without one.
✗
LoRA enables training on a larger dataset than full fine-tuning
Why it's wrong here
LoRA does not inherently allow larger datasets; it reduces per‑example memory but the dataset size is independent.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the misconception that parameter-efficient methods like LoRA improve inference speed, when in reality they primarily reduce memory during training and do not accelerate inference.

Detailed technical explanation

How to think about this question

Under the hood, LoRA decomposes the weight update ΔW into two low-rank matrices A and B (e.g., rank r=8), applied to the query and value projection matrices in attention. This means the number of trainable parameters scales with r * (d_in + d_out) instead of d_in * d_out, enabling fine-tuning of 175B parameter models on a single GPU. In practice, LoRA adapters can be hot-swapped for different tasks without duplicating the base model, which is critical for multi-tenant serving environments.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this Generative AI Leader question test?

Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: LoRA significantly reduces the number of trainable parameters, making fine-tuning more memory-efficient — LoRA (Low-Rank Adaptation) injects trainable low-rank matrices into the transformer layers while keeping the original model weights frozen. This drastically reduces the number of trainable parameters (often by 10,000x), which lowers GPU memory requirements for storing optimizer states and gradients during training, making fine-tuning feasible on consumer hardware.

What should I do if I get this Generative AI Leader question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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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.