- A
Increase the context window of the model
Why wrong: Context window is not relevant for classification of short text snippets.
- B
Apply semi-supervised learning by pseudo-labeling the unlabeled data
Semi-supervised learning can use the unlabeled data to improve the model by generating pseudo-labels.
- C
Use RAG to retrieve similar examples from the unlabeled corpus during inference
Why wrong: RAG is typically used for generation, not classification, and does not directly improve model performance.
- D
Train a model from scratch on the labeled data only
Why wrong: 500 examples are insufficient for training from scratch; overfitting is likely.
- E
Use a pre-trained LLM (e.g., Gemini) and fine-tune on the labeled data
Transfer learning from a pre-trained model works well with small labeled datasets.
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.
A data scientist wants to improve the performance of a text classification model for customer feedback. They have a small labeled dataset of 500 examples and a large unlabeled corpus of 100,000 feedback messages. Which TWO strategies would be most effective? (Choose 2)
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
Apply semi-supervised learning by pseudo-labeling the unlabeled data
Option B is correct because semi-supervised learning with pseudo-labeling leverages the large unlabeled corpus to augment the small labeled dataset. The model first trains on the 500 labeled examples, then generates pseudo-labels for the unlabeled data, and retrains on the combined set, effectively increasing the training signal without requiring manual annotation.
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.
- ✗
Increase the context window of the model
Why it's wrong here
Context window is not relevant for classification of short text snippets.
- ✓
Apply semi-supervised learning by pseudo-labeling the unlabeled data
Why this is correct
Semi-supervised learning can use the unlabeled data to improve the model by generating pseudo-labels.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use RAG to retrieve similar examples from the unlabeled corpus during inference
Why it's wrong here
RAG is typically used for generation, not classification, and does not directly improve model performance.
- ✗
Train a model from scratch on the labeled data only
Why it's wrong here
500 examples are insufficient for training from scratch; overfitting is likely.
- ✓
Use a pre-trained LLM (e.g., Gemini) and fine-tune on the labeled data
Why this is correct
Transfer learning from a pre-trained model works well with small labeled datasets.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that RAG is a universal solution for any data scarcity problem, but in this context, RAG does not improve the model's training signal and is instead used for retrieval during inference, not for semi-supervised learning.
Detailed technical explanation
How to think about this question
Pseudo-labeling works by using the model's own predictions on unlabeled data as training targets, often with a confidence threshold to filter noisy labels. In practice, this approach can be combined with consistency regularization (e.g., in the FixMatch algorithm) to enforce that the model produces similar predictions under different augmentations, which is especially effective when the labeled set is as small as 500 examples. Fine-tuning a pre-trained LLM (Option E) leverages transfer learning from massive corpora, requiring only a few hundred labeled examples to adapt to the specific classification task, making it highly data-efficient.
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: Apply semi-supervised learning by pseudo-labeling the unlabeled data — Option B is correct because semi-supervised learning with pseudo-labeling leverages the large unlabeled corpus to augment the small labeled dataset. The model first trains on the 500 labeled examples, then generates pseudo-labels for the unlabeled data, and retrains on the combined set, effectively increasing the training signal without requiring manual annotation.
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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