- A
The summarization domain is very specialized and requires unique terminology
Why wrong: Specialized domains often require fine-tuning for accuracy.
- B
The team lacks machine learning expertise to fine-tune a model
Pre-built APIs require no ML expertise, reducing time and risk.
- C
The expected usage volume is millions of API calls per day
Why wrong: High volume may favor fine-tuning to reduce per-call cost.
- D
The company has strict data residency requirements that prevent sending data to an API
Why wrong: Data residency may require on-premises or custom deployment.
- E
The company needs to launch the feature in two weeks
Pre-built APIs enable rapid integration, meeting tight deadlines.
Question 116 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 team is evaluating whether to build a custom fine-tuned model or use a pre-built API for a document summarization task. Which TWO factors most strongly support using a pre-built API?
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 team lacks machine learning expertise to fine-tune a model
Option B is correct because a pre-built API eliminates the need for in-house machine learning expertise, as the team can simply call the API endpoint without managing model training, hyperparameter tuning, or infrastructure. This is especially critical when the team lacks the specialized skills required for fine-tuning, such as data preparation, gradient computation, and evaluation on domain-specific metrics like ROUGE.
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.
- ✗
The summarization domain is very specialized and requires unique terminology
Why it's wrong here
Specialized domains often require fine-tuning for accuracy.
- ✓
The team lacks machine learning expertise to fine-tune a model
Why this is correct
Pre-built APIs require no ML expertise, reducing time and risk.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The expected usage volume is millions of API calls per day
Why it's wrong here
High volume may favor fine-tuning to reduce per-call cost.
- ✗
The company has strict data residency requirements that prevent sending data to an API
Why it's wrong here
Data residency may require on-premises or custom deployment.
- ✓
The company needs to launch the feature in two weeks
Why this is correct
Pre-built APIs enable rapid integration, meeting tight deadlines.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often overlook that high usage volume can make a custom model more economical at scale, but this question emphasizes that lack of ML expertise and time-to-market strongly favor using a pre-built API.
Detailed technical explanation
How to think about this question
Pre-built APIs like OpenAI's GPT-4 or Anthropic's Claude use a transformer-based architecture with billions of parameters, and they are accessed via RESTful endpoints (e.g., POST /v1/completions) with JSON payloads. Under the hood, these APIs handle batching, tokenization, and inference on shared GPU clusters, but they do not allow control over the model's weights or training data, which can be a problem for domain-specific tasks like legal or medical summarization where terminology precision is critical.
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.
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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: The team lacks machine learning expertise to fine-tune a model — Option B is correct because a pre-built API eliminates the need for in-house machine learning expertise, as the team can simply call the API endpoint without managing model training, hyperparameter tuning, or infrastructure. This is especially critical when the team lacks the specialized skills required for fine-tuning, such as data preparation, gradient computation, and evaluation on domain-specific metrics like ROUGE.
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
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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