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

Question 1

A product team uses a translation model to convert English product descriptions into French. The model mixes formal and informal French dialects. Which simple prompt modification likely solves this?

Accepted Answer

Add a system prompt specifying 'Use only formal French with no informal expressions.'. Adding a system prompt that explicitly instructs the model to 'Use only formal French with no informal expressions' directly constrains the output style at inference time without requiring retraining. This leverages the model's instruction-following capability to enforce a specific dialect, which is the simplest and most effective modification for controlling output style in a production translation pipeline.

Answer

Increase the temperature to encourage more consistent output.

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Fine-tune the model on a corpus of formal French texts.

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Provide a few-shot example of a formal French translation in the prompt.

Question 2

A content generation model for e-commerce product descriptions repeats the same phrases across multiple descriptions (e.g., 'high-quality', 'best-in-class'). The team wants more varied and engaging output. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty parameter to 1.0.. Increasing the frequency penalty to 1.0 penalizes tokens that have already appeared in the generated text, directly reducing repetition of phrases like 'high-quality' and 'best-in-class'. This encourages the model to use more diverse vocabulary and sentence structures, leading to varied and engaging product descriptions.

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Decrease the max output tokens to 50.

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Increase the temperature parameter to 1.5.

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Set the top-p value to a very small number like 0.1.

Question 3

Which TWO techniques effectively reduce bias in generative model outputs? (Choose two.)

Accepted Answer

Apply adversarial debiasing during training or fine-tuning.. Adversarial debiasing (A) directly reduces bias by training the model to minimize an adversary's ability to predict protected attributes from the model's outputs, forcing the model to learn representations that are invariant to those attributes. Fine-tuning on a balanced dataset (D) corrects representation bias by ensuring the model sees equal examples across groups, preventing overfitting to majority patterns. Both techniques actively address the root causes of bias in training data or model behavior.

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Increase the temperature parameter to introduce more variability.

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Use a larger model with more parameters.

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Reduce max output tokens to limit the model's expression.

Question 4

Which TWO methods are most effective for improving factual accuracy in a language model's responses? (Choose two.)

Accepted Answer

Use prompt engineering to instruct the model to rely on provided facts.. Prompt engineering can explicitly instruct the model to base its responses on provided facts, reducing reliance on parametric knowledge that may be outdated or incorrect. By including directives like 'Use only the information in the following text' or 'Answer based solely on the provided context,' the model is guided to prioritize given facts over its internal training data, which improves factual accuracy in the output.

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Decrease the temperature to make responses more deterministic.

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Increase top-k sampling to consider a wider range of tokens.

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Replace the model with a smaller, more focused model.

Question 5

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.. Self-consistency (A) improves quality by generating multiple candidate outputs from the same prompt and selecting the most consistent or frequent answer, reducing variance and errors, and iterative refinement lets the model revise its own output via feedback loops. In-context learning (B) with relevant few-shot examples steers the model toward the desired format, style, and reasoning pattern, improving output quality without retraining. Prompt chaining (D) decomposes a complex task into simpler sub-tasks, so each step is handled with a focused prompt and yields more coherent final outputs. By contrast, output safety filters (C) address appropriateness rather than quality or coherence, and random sampling (E) increases diversity but can reduce coherence, so neither is a quality-improvement technique.

Answer

Applying output safety filters to remove inappropriate content.

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Random sampling to increase output diversity.

Question 6

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.. Implementing a RAG (Retrieval-Augmented Generation) pipeline directly addresses the chatbot's inability to answer questions about new products or promotions. By retrieving relevant, up-to-date information from the company's product catalog and knowledge base and injecting it into the prompt, the model gains access to current data beyond its training cutoff. This approach keeps response times under 3 seconds (as retrieval is fast) and avoids the need for costly retraining, while the system instruction 'Answer based on your knowledge' is replaced with grounded context.

Answer

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

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Add additional safety filters to block irrelevant responses.

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

Question 7

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.. 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.

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

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

Question 8

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.. Few-shot prompting (A) is the most direct and efficient technique because it provides the model with concrete examples of the desired output style (award-winning, creative slogans) within the context window, guiding the model's generation without altering its underlying weights. This approach immediately constrains the output to be brand-specific and creative by leveraging in-context learning, while the 15-word limit can be handled via a simple instruction in the prompt, avoiding the need for fine-tuning or risky parameter changes.

Answer

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

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

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

Question 9

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.. It creates an automated feedback loop: the static analysis scanner detects vulnerabilities in the generated code, and the model revises the output based on that feedback. This approach directly mitigates security flaws without requiring manual review, preserving developer velocity. It leverages the scanner's precise, rule-based detection to iteratively improve the model's output, which is more reliable than relying on the model's inherent safety.

Answer

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

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

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

Question 10

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.. Implementing a Retrieval-Augmented Generation (RAG) pipeline with Vertex AI Search grounds the model's output in retrieved, authoritative medical documents. This directly addresses the root cause of hallucination—the model's reliance on its parametric memory—by providing factual context at inference time, which is far more effective for regulatory compliance than adjusting generation parameters or training data size alone.

Answer

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

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

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

Question 11

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.. Vertex AI Safety Filters provide out-of-the-box content moderation without modifying the model, and crafting system instructions (system-level prompts) can constrain the chatbot's behavior to stay on-topic and avoid offensive responses. This combination addresses both safety and relevance without requiring fine-tuning or altering model parameters.

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

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Set temperature to 0.0 and top_p to 0.1.

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Provide 50 few-shot examples of safe interactions.

Question 12

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.. Refining the prompt to be concise and include explicit instructions to stick to facts and limit output to 50 words directly addresses both issues without retraining. Prompt engineering is the most effective technique for controlling output length and reducing hallucinations in the Gemini API, as it guides the model's behavior through natural language constraints rather than altering generation parameters. Note that few-shot examples (Option C) are also a form of prompt engineering and could help, but the question asks for the most direct approach: explicit natural-language instructions in the prompt are the simplest and most reliable way to enforce a strict word limit and factual grounding, whereas few-shot examples alone do not guarantee a specific output length.

Answer

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

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

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Set temperature to 0.0 and top_k to 1.

Question 13

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.. Few-shot prompting provides the model with concrete examples of desired output structure and content, guiding it to include key financial metrics and trends without retraining. This technique leverages in-context learning, where the model generalizes from the examples in the prompt to produce more complete and relevant summaries, while avoiding the cost and latency of fine-tuning.

Answer

Increase temperature to 0.9 to encourage more creative outputs.

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Set stop sequences to ['

'] to ensure the model finishes each paragraph.

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Lower top_p to 0.5 to reduce the sampling pool.

Question 14

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.. Implementing a RAG pipeline with Vertex AI Search allows the chatbot to retrieve relevant troubleshooting documents from the internal corpus in real-time, grounding the LLM's responses in authoritative sources. This approach directly addresses the problem of plausible-sounding but incorrect answers for rare/novel issues without requiring retraining, and it keeps latency low by fetching only the most relevant documents before generation.

Answer

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

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

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

Question 15

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.. Increasing temperature from 0.2 to 0.9 raises the randomness of token sampling, which directly increases the diversity and novelty of generated text. A low temperature (e.g., 0.2) makes the model highly deterministic, always picking the most probable next token, leading to bland outputs. A higher temperature (e.g., 0.9) allows less probable tokens to be selected more often, producing more creative and varied taglines.

Answer

Decrease top_p from 1.0 to 0.5.

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Set frequency_penalty to 2.0.

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Decrease temperature from 0.7 to 0.2.