- A
Implement grounding by connecting to a knowledge base of current policies.
Grounding retrieves real-time information from the knowledge base.
- B
Use prompt engineering to instruct the model to say 'I don't know' if unsure.
Why wrong: This avoids wrong answers but doesn't provide correct information.
- C
Increase the context window to include more history.
Why wrong: Larger context does not add new knowledge.
- D
Fine-tune the model on the latest policy documents.
Why wrong: Fine-tuning requires retraining and doesn't adapt to rapidly changing policies.
Question 761 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. 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A chatbot built with Vertex AI PaLM API often provides outdated information about company policies because the training data is months old. Which approach should the team use?
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
✓
Implement grounding by connecting to a knowledge base of current policies.
Option A is correct because grounding connects the PaLM API to a live, authoritative knowledge base (e.g., Cloud Storage, BigQuery, or Vertex AI Search) containing the latest company policies. This allows the model to retrieve and cite current information at inference time without retraining, directly solving the staleness issue. Grounding is the recommended approach in Vertex AI for ensuring factual, up-to-date responses from a foundation model.
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.
- ✓
Implement grounding by connecting to a knowledge base of current policies.
Why this is correct
Grounding retrieves real-time information from the knowledge base.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use prompt engineering to instruct the model to say 'I don't know' if unsure.
Why it's wrong here
This avoids wrong answers but doesn't provide correct information.
- ✗
Increase the context window to include more history.
Why it's wrong here
Larger context does not add new knowledge.
- ✗
Fine-tune the model on the latest policy documents.
Why it's wrong here
Fine-tuning requires retraining and doesn't adapt to rapidly changing policies.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In the Google Gen AI Leader exam, a common trap is confusing grounding (dynamic knowledge injection at inference time) with fine-tuning (static model update). Candidates often assume fine-tuning is the best solution for real-time accuracy, but grounding is the correct approach when policies change frequently.
Detailed technical explanation
How to think about this question
Grounding in Vertex AI PaLM API works by appending retrieved documents from a specified data store (e.g., Vertex AI Search or a custom vector database) to the prompt context, allowing the model to generate responses based on those sources. Under the hood, the model uses a technique called 'retrieval-augmented generation' (RAG), where the retrieval step is handled by a separate embedding-based search, and the generation step is constrained to cite the retrieved passages. In a real-world scenario, a company with frequently updated HR policies would use grounding to ensure the chatbot always references the latest version, avoiding the latency and cost of repeated fine-tuning.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement grounding by connecting to a knowledge base of current policies. — Option A is correct because grounding connects the PaLM API to a live, authoritative knowledge base (e.g., Cloud Storage, BigQuery, or Vertex AI Search) containing the latest company policies. This allows the model to retrieve and cite current information at inference time without retraining, directly solving the staleness issue. Grounding is the recommended approach in Vertex AI for ensuring factual, up-to-date responses from a foundation model.
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
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