Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 5 — 15 Questions

Question 1

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.

Answer

Increase the temperature parameter to introduce more variability.

Answer

Use a larger model with more parameters.

Answer

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

Question 2

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. Iterative refinement further enhances coherence by allowing the model to revise its own output based on feedback loops, such as chain-of-thought self-correction.

Answer

Applying output safety filters to remove inappropriate content.

Answer

Random sampling to increase output diversity.

Question 3

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.

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 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.. 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.

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.. 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.

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 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.

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 7

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.

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 8

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).. Fine-tuning with RLHF directly optimizes the model to avoid toxic outputs by using human feedback as a reward signal, aligning the model's behavior with safety guidelines without degrading its generative performance. This technique adjusts the model's weights to reduce harmful patterns while preserving its ability to generate coherent and contextually appropriate emails.

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 9

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. Providing few-shot examples of ideal summaries (A) is effective because it gives the model concrete examples of the desired output format, style, and level of detail, which helps guide the model's generation through in-context learning. Using a larger, more capable model (B) directly improves the model's capacity to understand complex relationships and generate coherent, accurate summaries, as larger models typically have better reasoning and language understanding capabilities.

Answer

Increasing max output length significantly

Answer

Setting top_p to 0.1

Answer

Adjusting temperature to 0.8

Question 10

A financial technology company has deployed a custom-tuned PaLM 2 model on Vertex AI to generate personalized investment recommendations for retail clients. The model was fine-tuned on a corpus of historical market data and advisory transcripts. Recently, the compliance team flagged that several recommendations contradicted SEC guidelines, and the model sometimes repeated prohibited statements from outdated training materials. The team has already implemented safety filters (e.g., blocking toxic content) and adjusted the model's system instructions to be more conservative. However, the issues persist. The model's deployment parameters are: temperature=0.4, top_p=0.9, max_output_tokens=500, and no grounding. The company must maintain compliance without significantly increasing latency. What should they do next?

Accepted Answer

Configure Vertex AI grounding using a curated data store of real-time SEC regulations and market data. Configuring Vertex AI grounding with a curated data store of real-time SEC regulations directly addresses the root cause: the model is generating outputs that contradict current compliance rules. Grounding forces the model to base its responses on authoritative, up-to-date sources, which is more effective than safety filters or system instructions alone, and it avoids the latency increase of a second model or the risk of catastrophic forgetting from additional fine-tuning.

Answer

Increase temperature to 0.7 to allow more diverse responses, and add a second model to verify outputs

Answer

Perform an additional fine-tuning round exclusively on the most recent SEC regulatory filings and compliance-approved content

Answer

Implement a chain-of-thought prompting technique that requires the model to explain its reasoning step by step

Question 11

A team is building a generative AI model for customer support. They notice the model often produces overly polite but unhelpful responses. Which technique would best improve response quality without sacrificing helpfulness?

Accepted Answer

Apply reinforcement learning from human feedback (RLHF). RLHF directly addresses the misalignment between the model's training objective (e.g., predicting the next token) and the desired outcome (helpful, not just polite). By using human feedback to train a reward model, the system learns to optimize for response quality and helpfulness, reducing sycophantic or overly polite but uninformative outputs.

Answer

Increase the amount of training data

Answer

Lower the top_k sampling value

Answer

Increase the temperature parameter

Question 12

A generative AI model for code generation sometimes produces syntactically incorrect code. The team wants to reduce syntax errors without retraining the entire model. Which approach is most effective?

Accepted Answer

Implement constrained decoding with grammar rules. Constrained decoding with grammar rules directly enforces the syntax of the target programming language during token generation, preventing the model from producing invalid constructs. This approach modifies the decoding process (e.g., using a context-free grammar or a formal syntax specification) to mask or forbid tokens that would lead to a syntax error, without altering the underlying model weights. It is the most effective method because it guarantees syntactically correct output at generation time, rather than relying on post-hoc fixes or probabilistic adjustments.

Answer

Run a syntax checker after generation and regenerate

Answer

Add a system prompt that instructs the model to produce valid code

Answer

Increase beam search width

Question 13

A generative AI model for chatbot responses sometimes produces toxic language. The team wants to reduce toxicity without significantly affecting the model's helpfulness. Which approach is best?

Accepted Answer

Fine-tune with a dataset of non-toxic responses and use RLHF. Fine-tuning with a curated dataset of non-toxic responses directly adjusts the model's weights to reduce the likelihood of generating toxic language, while RLHF (Reinforcement Learning from Human Feedback) further aligns the model with human preferences for helpfulness and safety. This combined approach addresses the root cause of toxicity in the model's behavior without the blunt trade-offs of other methods, preserving the model's utility.

Answer

Increase the temperature parameter

Answer

Reduce the maximum output tokens

Answer

Apply a toxicity classifier as a post-processing filter

Question 14

A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG) with product database. Retrieval-augmented generation (RAG) is the best technique because it dynamically retrieves relevant, up-to-date product specifications from a trusted database at inference time, grounding the model's output in verified facts. This directly addresses factual inconsistency by ensuring the generated description is based on authoritative source data rather than relying solely on the model's parametric memory.

Answer

Enhance the system prompt with product details

Answer

Lower the temperature to 0.0

Answer

Fine-tune the model on product descriptions

Question 15

A team is fine-tuning a large language model for medical advice. Which TWO techniques are most effective for improving the safety and reliability of the model's outputs?

Accepted Answer

Constitutional AI. Constitutional AI (A) is correct because it embeds a set of ethical principles directly into the model's training process, allowing the model to self-critique and revise its outputs to avoid harmful or unsafe medical advice. This technique proactively enforces safety constraints without requiring extensive human labeling, making it highly effective for high-stakes domains like healthcare.

Answer

Lowering the temperature to 0.0

Answer

Increasing training data size

Answer

Increasing top_p to 1.0