- A
The model was trained on a dataset with many insecure code examples
Training data bias toward insecure code can cause the model to suggest vulnerabilities.
- B
The model's temperature is set too low
Why wrong: Low temperature makes outputs more deterministic, not insecure.
- C
The model is too small for code generation tasks
Why wrong: Model size is not directly linked to suggesting vulnerabilities.
- D
The prompt does not include a security constraint
Why wrong: Missing security constraint might reduce caution, but the root cause is training data.
Question 601 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 deploys a GenAI-powered code review assistant. During evaluation, they find that the assistant often suggests security vulnerabilities as improvements. 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
✓
The model was trained on a dataset with many insecure code examples
The most likely cause is that the model was trained on a dataset containing many insecure code examples. A GenAI code review assistant learns patterns from its training data; if that data includes prevalent security vulnerabilities (e.g., SQL injection, buffer overflows), the model will internalize those patterns as 'normal' or even 'desirable' improvements. This leads to the assistant suggesting insecure code changes because it is statistically replicating the flawed logic it was exposed to during training.
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 model was trained on a dataset with many insecure code examples
Why this is correct
Training data bias toward insecure code can cause the model to suggest vulnerabilities.
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.
- ✗
The model's temperature is set too low
Why it's wrong here
Low temperature makes outputs more deterministic, not insecure.
- ✗
The model is too small for code generation tasks
Why it's wrong here
Model size is not directly linked to suggesting vulnerabilities.
- ✗
The prompt does not include a security constraint
Why it's wrong here
Missing security constraint might reduce caution, but the root cause is training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that prompt engineering alone (e.g., adding a security constraint) can override fundamental training data biases, when in fact the model's learned weights from the training corpus are the dominant factor in output quality.
Trap categories for this question
Command / output trap
Low temperature makes outputs more deterministic, not insecure.
Detailed technical explanation
How to think about this question
Under the hood, large language models (LLMs) learn probability distributions over tokens from their training corpus. If the training data contains insecure code snippets (e.g., from public repositories with known CVEs), the model's attention mechanisms and weight matrices encode those insecure patterns as high-probability sequences. During inference, the model's autoregressive generation samples from this distribution, making insecure suggestions statistically likely. In real-world scenarios, this is why fine-tuning on curated, security-reviewed datasets (e.g., using OWASP guidelines) is critical before deploying a code review assistant.
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.
- →
Applying Generative AI in Business — study guide chapter
Learn the concepts, then practise the questions
- →
Applying Generative AI in Business practice questions
Targeted practice on this topic area only
- →
All Generative AI Leader questions
997 questions across all exam domains
- →
Google Cloud Generative AI Leader Generative AI Leader study guide
Full concept coverage aligned to exam objectives
- →
Generative AI Leader practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related Generative AI Leader practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Fundamentals of Generative AI practice questions
Practise Generative AI Leader questions linked to Fundamentals of Generative AI.
Business Strategies for Generative AI Solutions practice questions
Practise Generative AI Leader questions linked to Business Strategies for Generative AI Solutions.
Generative AI Concepts and Technologies practice questions
Practise Generative AI Leader questions linked to Generative AI Concepts and Technologies.
Google AI Ecosystem and Strategy practice questions
Practise Generative AI Leader questions linked to Google AI Ecosystem and Strategy.
Responsible AI and Data Governance practice questions
Practise Generative AI Leader questions linked to Responsible AI and Data Governance.
Google Cloud's Generative AI Offerings practice questions
Practise Generative AI Leader questions linked to Google Cloud's Generative AI Offerings.
Techniques to Improve Generative AI Model Output practice questions
Practise Generative AI Leader questions linked to Techniques to Improve Generative AI Model Output.
Applying Generative AI in Business practice questions
Practise Generative AI Leader questions linked to Applying Generative AI in Business.
Generative AI Leader fundamentals practice questions
Practise Generative AI Leader questions linked to Generative AI Leader fundamentals.
Generative AI Leader scenario practice questions
Practise Generative AI Leader questions linked to Generative AI Leader scenario.
Generative AI Leader troubleshooting practice questions
Practise Generative AI Leader questions linked to Generative AI Leader troubleshooting.
Practice this exam
Start a free Generative AI Leader practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 model was trained on a dataset with many insecure code examples — The most likely cause is that the model was trained on a dataset containing many insecure code examples. A GenAI code review assistant learns patterns from its training data; if that data includes prevalent security vulnerabilities (e.g., SQL injection, buffer overflows), the model will internalize those patterns as 'normal' or even 'desirable' improvements. This leads to the assistant suggesting insecure code changes because it is statistically replicating the flawed logic it was exposed to during training.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.