Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 8 — 15 Questions

Question 1

Which THREE techniques are commonly used to improve the overall quality and coherence of generative model outputs? (Choose three.)

Accepted Answer

Using self-consistency or iterative refinement to choose the best output.. Options A, B, and E are correct. A: few-shot prompting provides examples that improve output structure. B: prompt chaining breaks complex tasks into steps, enhancing coherence. E: iterative refinement (e.g., self-consistency) improves quality by generating multiple outputs and selecting the best. C is wrong because random sampling degrades quality. D is wrong because safety filters only block harmful content, not improve quality.

Answer

Applying output safety filters to remove inappropriate content.

Answer

Random sampling to increase output diversity.

Question 2

A large e-commerce company deploys a generative AI chatbot on Vertex AI for customer service. The chatbot is powered by a fine-tuned model on the company's historical support tickets. Despite high accuracy on training topics, the chatbot frequently gives irrelevant or off-topic answers when customers ask about new products or promotions. The company maintains a comprehensive product catalog and a knowledge base of current promotions. The chatbot's prompts include a system instruction to 'Answer based on your knowledge' and no other retrieval mechanism. The response time requirement is under 3 seconds. Which course of action should the team take?

Accepted Answer

Implement a RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.. Option C is correct because implementing RAG with the product catalog allows real-time retrieval of current information, addressing the irrelevance for new products without needing retraining. Option A is wrong because fine-tuning again on outdated data won't help with new products. Option B is wrong because increasing temperature makes outputs more random and less focused. Option D is wrong because adding more safety filters doesn't improve topical relevance.

Answer

Increase the temperature to encourage the model to generate more diverse answers.

Answer

Add additional safety filters to block irrelevant responses.

Answer

Fine-tune the model again on a larger dataset that includes recent support tickets.

Question 3

A healthcare startup uses a generative model fine-tuned on general medical literature to provide preliminary diagnostic suggestions from patient text. The model frequently misses rare diseases and sometimes suggests common conditions that are unlikely given the symptoms. The startup has a curated dataset of rare disease case reports and wants to improve the model’s sensitivity to rare conditions without sacrificing overall accuracy. They cannot afford to retrain the entire model from scratch. The model is deployed on Vertex AI Prediction with low latency requirement. Which approach should they take?

Accepted Answer

Implement a human-in-the-loop system: for outputs with low confidence or suspected rare disease, route to a human expert.. Option D is correct because implementing a human-in-the-loop process for rare disease flags combines AI with expert review, catching misses while maintaining speed for common cases. Option A is wrong because prompt engineering alone may not teach the model about rare diseases. Option B is wrong because increasing top-p restricts vocabulary but doesn't inject knowledge. Option C is wrong because fine-tuning again might cause catastrophic forgetting of common conditions.

Answer

Perform continued fine-tuning on the rare disease dataset using a low learning rate.

Answer

Add a system prompt instructing the model to consider rare diseases more carefully.

Answer

Reduce top-p sampling to focus on high-probability tokens, assuming rare diseases have lower probability.

Question 4

A marketing agency uses a generative AI model to create slogans for ad campaigns. The model outputs generic slogans like 'Quality you can trust' that lack originality. The agency has a library of past award-winning slogans and wants to generate more creative and brand-specific outputs. They have a requirement that the model must not produce slogans longer than 15 words. Which technique should they prioritize?

Accepted Answer

Use few-shot prompting with 3-5 examples of award-winning slogans in the prompt.. Option C is correct because providing few-shot examples of award-winning slogans in the prompt directly inspires creativity and style matching. Option A is wrong because increasing temperature may produce nonsense. Option B is wrong because fine-tuning is heavy and not needed. Option D is wrong because token limit only truncates length, doesn't improve creativity.

Answer

Set max tokens to 15 to force shorter, potentially more punchy slogans.

Answer

Increase the temperature to 1.2 to encourage more creative word combinations.

Answer

Fine-tune the model on the library of award-winning slogans.

Question 5

A software development team builds an internal code assistant using a generative model. The assistant writes Python functions that often contain security vulnerabilities such as SQL injection or command injection. The team wants to mitigate these vulnerabilities without adding a manual review step for every code snippet, as that would slow development. They have access to a static analysis security scanner API. Which approach best addresses the vulnerabilities while maintaining developer velocity?

Accepted Answer

After each generation, automatically run the code through the static analysis scanner, and if vulnerabilities are found, send the output back to the model for revision with the scanner's feedback.. Option D is correct because post-generation automatic scanning with the security scanner catches vulnerabilities and can request regeneration with suggestions, maintaining speed. Option A is wrong because fine-tuning may not eliminate all vulnerabilities. Option B is wrong because top-k only affects output diversity, not security. Option C is wrong because system prompts are not reliably followed for security.

Answer

Increase top-k sampling to generate a wider variety of code tokens.

Answer

Fine-tune the model on a corpus of secure code examples.

Answer

Add a system prompt: 'Do not generate code with security vulnerabilities.'

Question 6

A healthcare startup has fine-tuned a Vertex AI PaLM 2 model on a dataset of medical records to generate patient summaries. The model produces fluent text but occasionally fabricates diagnoses not present in the input. The team has already tried increasing the training data size by 20% and adjusting the temperature from 0.7 to 0.2, but hallucinations persist. The summaries must be factually accurate for regulatory compliance. What should the team do next?

Accepted Answer

Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.. Option B is correct because augmenting the model with a retrieval-augmented generation (RAG) pipeline grounded in a trusted medical knowledge base directly addresses hallucination by forcing the model to reference verified sources. Option A is wrong because changing the base model does not solve the fundamental lack of grounding. Option C is wrong because few-shot examples improve output format but not factual accuracy. Option D is wrong because increasing the context window does not prevent fabrication; it may even introduce more irrelevant information.

Answer

Increase the maximum output tokens to allow the model to generate more detailed summaries.

Answer

Add more few-shot examples to the prompt for each generation.

Answer

Switch the base model to Gemini 1.5 Pro without additional changes.

Question 7

A retail company is deploying a generative AI chatbot on Vertex AI to provide product recommendations. The chatbot uses a base foundation model with no fine-tuning. Users report that the chatbot sometimes gives offensive or insensitive responses. The team must quickly implement safety controls without modifying the model. They also want to reduce irrelevant off-topic answers. Which combination of techniques should they apply?

Accepted Answer

Enable Vertex AI Safety Filters and craft system instructions defining appropriate behavior.. Option D is correct because safety filters (e.g., Vertex AI Safety Settings) block harmful content, and prompt engineering with system instructions keeps the model on topic and respectful. Option A is wrong because temperature adjustment alone does not prevent toxicity. Option B is wrong because few-shot examples may not cover all safety scenarios. Option C is wrong because fine-tuning is not allowed per the constraint (no model modification).

Answer

Fine-tune the model on a curated dataset of safe retail conversations.

Answer

Set temperature to 0.0 and top_p to 0.1.

Answer

Provide 50 few-shot examples of safe interactions.

Question 8

A data scientist is using the Gemini API to generate product descriptions for an e-commerce site. The descriptions are often too verbose and include speculative claims that are not in the product specifications. The scientist wants to reduce hallucinations and control the length of the output without retraining the model. What should they do?

Accepted Answer

Refine the prompt to be concise and include instructions to stick to facts and limit output to 50 words.. Option A is correct because verbose prompts often lead to verbose output; simplifying the prompt reduces length. Adding explicit constraints like 'only use provided facts' reduces hallucination. Option B is wrong because increasing token count would make descriptions longer, not shorter. Option C is wrong because lower temperature reduces randomness but doesn't prevent speculation. Option D is wrong because few-shot examples may help but do not directly enforce length or factuality.

Answer

Increase the max output token count to 2048 and decrease temperature to 0.1.

Answer

Add three few-shot examples of short, factual descriptions.

Answer

Set temperature to 0.0 and top_k to 1.

Question 9

A financial services firm is using a foundation model on Vertex AI to generate investment summaries from quarterly reports. The summaries are accurate but often miss key financial metrics and trends. The team cannot afford to fine-tune the model frequently. Which technique should they use to improve the completeness and relevance of the summaries without modifying the model?

Accepted Answer

Provide three few-shot examples in the prompt that highlight the desired metrics.. Option C is correct because adding few-shot examples that specifically include the desired metrics (e.g., revenue growth, profit margins) trains the model to include those details. Option A is wrong because increasing temperature increases randomness, which could omit key facts. Option B is wrong because stopping at newlines doesn't guarantee completeness. Option D is wrong because adjusting top_p does not target completeness.

Answer

Increase temperature to 0.9 to encourage more creative outputs.

Answer

Set stop sequences to ['

'] to ensure the model finishes each paragraph.

Answer

Lower top_p to 0.5 to reduce the sampling pool.

Question 10

An AI team is building a customer support chatbot for a telecom company using a fine-tuned LLM on Vertex AI. The model performs well on common issues but fails to answer correctly for rare or novel problems, often providing plausible-sounding but incorrect solutions. The team has a large corpus of internal troubleshooting documents. They want to minimize incorrect answers while keeping latency low. Which approach should they take?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.. Option B is correct because RAG uses the troubleshooting documents as a knowledge base, providing grounded answers for rare issues without retraining. Option A is wrong because more fine-tuning on common issues won't help with rare ones. Option C is wrong because a larger model may increase latency and cost without solving the grounding problem. Option D is wrong because few-shot examples cannot cover all rare scenarios.

Answer

Switch to a larger base model (e.g., Gemini Ultra) without any retrieval.

Answer

Collect more data on rare issues and continue fine-tuning the model weekly.

Answer

Use a few-shot prompt with 10 examples of rare problems and solutions.

Question 11

A developer is using the Gemini API to generate creative product taglines. The taglines are often bland and uncreative. The developer wants more variety and novelty in the outputs. Which parameter adjustment would most effectively increase the diversity of the generated taglines?

Accepted Answer

Increase temperature from 0.2 to 0.9.. Option A is correct because higher temperature increases the randomness and creativity of the output. Option B is wrong because lower temperature makes output more deterministic and less creative. Option C is wrong because lower top_p reduces diversity. Option D is wrong because a high frequency penalty may discourage novel words, reducing creativity.

Answer

Decrease top_p from 1.0 to 0.5.

Answer

Set frequency_penalty to 2.0.

Answer

Decrease temperature from 0.7 to 0.2.

Question 12

A company is deploying a generative AI system that generates customer-facing emails. The system must ensure outputs are not toxic, biased, or harmful. Which TWO techniques are most effective for reducing toxicity in model outputs without significantly affecting performance?

Accepted Answer

Fine-tune the model on a dataset of safe emails using reinforcement learning from human feedback (RLHF).. Options A and D are correct. Fine-tuning with RLHF (or using a safety-tuned model) directly aligns the model to avoid toxic outputs. Output filtering (e.g., safety classifiers) provides a robust post-processing layer. Option B (temperature) does not prevent toxicity, only randomness. Option C (few-shot) is insufficient for safety. Option E (increasing tokens) may increase risk.

Answer

Increase the maximum output token count to allow more context.

Answer

Set temperature to a very low value (e.g., 0.1).

Answer

Provide 50 few-shot examples of safe emails in every prompt.

Question 13

A company uses a text generation model for customer support but notices it occasionally provides outdated information. Which technique should they implement to improve output accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG). Option B is correct because RAG (Retrieval-Augmented Generation) retrieves current information from a knowledge base, ensuring factual accuracy. Option A (temperature increase) would increase randomness, making outputs less reliable. Option C (fine-tuning with historical data) might not include recent updates. Option D (max tokens) only affects length, not accuracy.

Answer

Increase max output tokens

Answer

Fine-tune the model with more historical support data

Answer

Increase model temperature to 1.0

Question 14

Which TWO techniques are most effective for improving the quality of a generative AI model's output when summarizing complex documents?

Accepted Answer

Providing few-shot examples of ideal summaries. Options B and D are correct. Few-shot examples guide the model to desired output format and consistency. Using a larger, more capable model often yields better summaries due to deeper language understanding. Option A (temperature adjustment) is less critical for summaries. Option C (max output length) affects length, not quality. Option E (low top_p) may restrict output too much.

Answer

Increasing max output length significantly

Answer

Setting top_p to 0.1

Answer

Adjusting temperature to 0.8

Question 15

The exhibit shows the deployment configuration for a conversational AI model used in a finance application. Users report that responses are creative but often contain factually incorrect financial advice. Which parameter change would most improve factual accuracy?

Accepted Answer

Add grounding sources, such as "EnterpriseSearch" or "Web". Option B is correct because grounding sources (e.g., Google Search or a knowledge base) inject real-world facts into responses, reducing hallucination. Option A (lower temperature) would reduce creativity but not directly fix factual inaccuracies. Option C (increased max tokens) addresses length, not facts. Option D (top_p increase) would make output even more variable.

Answer

Lower temperature to 0.1

Answer

Increase topP to 1.0

Answer

Increase maxOutputTokens to 1024