- A
The top-p sampling is too low, limiting the model's vocabulary
Why wrong: Top-p affects token selection, not the recency of knowledge.
- B
The temperature setting is too high, causing creative but incorrect outputs
Why wrong: Temperature affects randomness, not knowledge of recent APIs.
- C
The context window is too short to include relevant API documentation
Why wrong: Even with a longer context, the model cannot generate information it never learned.
- D
Codey's training data has a knowledge cutoff date before the deprecation
The model's pre-training data cutoff means it does not know about recent deprecations.
Question 162 of 997
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 developer is building a code generation assistant using Codey. They notice that the generated code sometimes contains deprecated API calls. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Codey's training data has a knowledge cutoff date before the deprecation
Option D is correct because Codey, like all large language models, is trained on a static dataset with a specific knowledge cutoff date. If the training data predates the deprecation of certain APIs, the model will not be aware of the newer, recommended alternatives and will continue to generate code using the deprecated calls. This is a fundamental limitation of the model's training data recency, not a parameter tuning issue.
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 top-p sampling is too low, limiting the model's vocabulary
Why it's wrong here
Top-p affects token selection, not the recency of knowledge.
- ✗
The temperature setting is too high, causing creative but incorrect outputs
Why it's wrong here
Temperature affects randomness, not knowledge of recent APIs.
- ✗
The context window is too short to include relevant API documentation
Why it's wrong here
Even with a longer context, the model cannot generate information it never learned.
- ✓
Codey's training data has a knowledge cutoff date before the deprecation
Why this is correct
The model's pre-training data cutoff means it does not know about recent deprecations.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse model parameter settings (like temperature or top-p) with the model's training data limitations, leading them to incorrectly select options A or B instead of recognizing the knowledge cutoff as the root cause.
Detailed technical explanation
How to think about this question
Under the hood, Codey's training corpus is frozen at a specific point in time, meaning any API deprecations occurring after that date are absent from the model's parameters. This is a common issue with all static LLMs, where the model cannot dynamically update its knowledge without retraining or fine-tuning. In a real-world scenario, a developer might need to use retrieval-augmented generation (RAG) to inject up-to-date API documentation into the prompt to mitigate this limitation.
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: Codey's training data has a knowledge cutoff date before the deprecation — Option D is correct because Codey, like all large language models, is trained on a static dataset with a specific knowledge cutoff date. If the training data predates the deprecation of certain APIs, the model will not be aware of the newer, recommended alternatives and will continue to generate code using the deprecated calls. This is a fundamental limitation of the model's training data recency, not a parameter tuning issue.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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