- A
Disable grounding and rely solely on the model's internal knowledge
Why wrong: Disabling grounding may reduce latency but will harm answer quality and does not address model size.
- B
Increase the batch size for requests
Why wrong: Batch size affects throughput but not per-request latency or cost for a model that is too large.
- C
Switch to a smaller model from Vertex AI Model Garden
Model Garden provides access to many model sizes; testing a smaller, faster model can reduce latency and cost.
- D
Fine-tune the existing model on the company's documents
Why wrong: Fine-tuning adds cost and time; it does not address the core issue of using an oversized model.
Question 489 of 997
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. 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.
A company is running a GenAI proof-of-concept (PoC) for internal document Q&A. The PoC shows high latency and cost. The team suspects they are using an unnecessarily large model for the task. What is the BEST action to optimize?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Switch to a smaller model from Vertex AI Model Garden
Switching to a smaller model from Vertex AI Model Garden directly addresses the root cause of high latency and cost: an unnecessarily large model. Smaller models have fewer parameters, requiring less compute per inference, which reduces both response time and operational expense while often being sufficient for domain-specific tasks like internal document Q&A.
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.
- ✗
Disable grounding and rely solely on the model's internal knowledge
Why it's wrong here
Disabling grounding may reduce latency but will harm answer quality and does not address model size.
- ✗
Increase the batch size for requests
Why it's wrong here
Batch size affects throughput but not per-request latency or cost for a model that is too large.
- ✓
Switch to a smaller model from Vertex AI Model Garden
Why this is correct
Model Garden provides access to many model sizes; testing a smaller, faster model can reduce latency and cost.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tune the existing model on the company's documents
Why it's wrong here
Fine-tuning adds cost and time; it does not address the core issue of using an oversized model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that fine-tuning or disabling features like grounding can solve performance issues, when the real bottleneck is model size and compute efficiency.
Detailed technical explanation
How to think about this question
Model size directly correlates with FLOPs per inference; for example, a 175B-parameter model like GPT-3 requires ~350 TFLOPS per forward pass, while a 7B-parameter model like Llama 2 requires ~14 TFLOPS. In Vertex AI Model Garden, smaller models such as Gemma 2B or PaLM 2 Gecko can be deployed with lower memory footprint and faster token generation, often achieving sub-second latency for document Q&A tasks when combined with retrieval-augmented generation (RAG) using a vector database like Vertex AI Vector Search.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Switch to a smaller model from Vertex AI Model Garden — Switching to a smaller model from Vertex AI Model Garden directly addresses the root cause of high latency and cost: an unnecessarily large model. Smaller models have fewer parameters, requiring less compute per inference, which reduces both response time and operational expense while often being sufficient for domain-specific tasks like internal document Q&A.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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