Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 7 — 15 Questions

Question 1

A development team is integrating a large language model into a healthcare application. They need to reduce the risk of generating harmful medical advice. Which THREE measures should they implement? (Choose three.)

Accepted Answer

Use a safety filter to block outputs containing harmful medical terminology.. Options A, B, and C are correct. A safety filter blocks outputs containing harmful terminology, fine-tuning on a curated medical dataset improves domain knowledge and safety, and RAG with trusted sources grounds outputs in verified information. Option D (high temperature) increases randomness and risk. Option E (disclaimer) does not reduce the generation of harmful advice.

Answer

Include a disclaimer in the system instruction that the model is not a doctor.

Answer

Set the temperature to a very high value to ensure diverse outputs.

Question 2

A company is using a large language model for automated translation of legal contracts. They find that the translations sometimes alter the meaning of specific clauses. Which TWO approaches would most effectively preserve the original meaning? (Choose two.)

Accepted Answer

Fine-tune the model on a parallel corpus of legal translations.. Options B and D are correct. Using a glossary of key legal terms with their translations ensures consistent terminology, and fine-tuning on a parallel corpus of legal translations adapts the model to the domain. Option A (full context in one prompt) may exceed token limits. Option C (high temperature) increases creativity and risk of altering meaning. Option E (low top-p) restricts vocabulary but does not preserve meaning of specific clauses.

Answer

Provide the full contract context in a single prompt.

Answer

Set top-p=0.1 to limit the vocabulary to the most likely tokens.

Answer

Increase the temperature to allow more creative phrasing.

Question 3

A developer wants to improve the factual accuracy of the model's summaries. Based on the exhibit, what should they do?

Accepted Answer

Configure grounding with a knowledge base.. Option A is correct because GROUNDING_CONFIG is NONE, so enabling grounding with a knowledge base would allow the model to retrieve factual information. Option B (enable support engine) is a different feature. Option C (re-train) is possible but more resource-intensive. Option D (increase context window) does not directly improve factual accuracy.

Answer

Enable the support engine.

Answer

Increase the model's context window.

Answer

Re-train the model with a dataset of facts.

Question 4

A user reports that the model's response to the same prompt varies significantly across different calls. Which parameter change would most likely reduce variability?

Accepted Answer

Decrease temperature to 0.2.. Option A is correct because decreasing temperature reduces randomness and makes outputs more deterministic. Option B (increase maxOutputTokens) does not affect variability. Option C (increase candidateCount) returns multiple candidates but each still varies. Option D (decrease topK) reduces diversity but temperature has a stronger effect.

Answer

Decrease topK to 10.

Answer

Increase candidateCount to 3.

Answer

Increase maxOutputTokens to 2000.

Question 5

A developer is building a chatbot for a medical application that discusses sensitive health topics. The chatbot consistently gets its outputs blocked. What should the developer do?

Accepted Answer

Review and refine the system instructions to avoid triggering safety filters, and consider using a different model endpoint that allows medical contexts.. Option C is correct because reviewing and refining system instructions to avoid triggering safety filters, and possibly using a different model endpoint that allows medical contexts, directly addresses the filter blocks. Option A (disable safety filter) violates policy. Option B (adjust thresholds to allow VIOLENCE/SEXUAL) is inappropriate for medical context and may violate guidelines. Option D (increase token limit) does not help with safety blocks.

Answer

Disable the safety filter entirely to allow all topics.

Answer

Adjust the safety category thresholds to allow VIOLENCE and SEXUAL content since it's medical.

Answer

Increase the input token limit to 2000.

Question 6

A company deploys a sentiment analysis model to classify customer reviews. The model consistently returns overly positive sentiment for all reviews, even when reviews contain negative feedback. Which technique would best resolve this issue?

Accepted Answer

Add a system prompt instructing the model to analyze the review for both positive and negative sentiment and output the overall classification.. Option C is correct because using a system prompt with explicit instructions to detect all sentiments, including negative, guides the model to consider the full emotional range. Option A is wrong because increasing temperature adds randomness and doesn't enforce balance. Option B is wrong because adjusting max tokens only affects output length. Option D is wrong because fine-tuning on a balanced dataset is a good practice but is not the quickest fix; prompt engineering is more immediate.

Answer

Fine-tune the model on a dataset with an equal number of positive and negative examples.

Answer

Reduce the max output tokens to limit the model's tendency to generate positive language.

Answer

Increase the temperature parameter to reduce model confidence.

Question 7

A developer is building a customer support chatbot using a large language model. The chatbot frequently generates plausible-sounding but incorrect answers to product questions. Which technique should be applied to improve factual accuracy?

Accepted Answer

Enable Grounding with the company's product knowledge base.. Option D is correct because Grounding (e.g., using Vertex AI Grounding with Search) retrieves relevant information from a trusted source in real time, reducing hallucination. Option A is wrong because increasing context length may include more irrelevant information and does not guarantee accuracy. Option B is wrong because higher temperature increases randomness, worsening hallucinations. Option C is wrong because few-shot prompting can help but only if examples are accurate and relevant; it does not dynamically look up facts.

Answer

Provide a few-shot example of correct answers in the prompt.

Answer

Use a higher temperature setting to encourage more creative responses.

Answer

Increase the model's context length to include more of the conversation history.

Question 8

A data scientist fine-tunes a generative image captioning model to describe medical images. The model outputs safe but very generic captions (e.g., 'An image of cells'). The goal is to produce more specific, clinically relevant descriptions. Which approach is most effective?

Accepted Answer

Perform incremental fine-tuning on a curated dataset of detailed medical image captions.. Option A is correct because incremental fine-tuning on a high-quality dataset of specific medical captions directly teaches the model the desired level of detail. Option B is wrong because increasing temperature may add irrelevant words, not specific clinical terms. Option C is wrong because top-k sampling can reduce output space but does not guarantee medical accuracy. Option D is wrong because beam search is for diverse output but does not address specificity.

Answer

Use diverse beam search during decoding to generate multiple caption candidates.

Answer

Adjust top-k sampling to restrict the vocabulary to medical terms only.

Answer

Increase the temperature to encourage the model to output longer, more varied captions.

Question 9

A team uses a generative model to summarize lengthy legal documents. The summaries are accurate but often exceed the target length of 200 words, varying widely. Which simple adjustment should be applied to ensure consistent output length?

Accepted Answer

Set the max output tokens parameter to 200.. Option B is correct because setting a maximum token limit directly controls the output length. Option A is wrong because temperature affects creativity, not length. Option C is wrong because prompt engineering can request a specific length but the model may not strictly follow it; token limit is more reliable. Option D is wrong because fine-tuning is heavy and may not be needed when a simple constraint works.

Answer

Fine-tune the model on summaries that are exactly 200 words.

Answer

Add a system prompt that says 'Summarize in exactly 200 words.'

Answer

Lower the temperature to reduce variability in word choices.

Question 10

A developer uses a code generation model to write Python functions. The output frequently contains syntax errors due to incorrect braces and indentation. Which technique should be used to produce syntactically valid code?

Accepted Answer

Apply constrained decoding techniques that enforce a grammar for the target programming language.. Option A is correct because constrained decoding (e.g., with guidance or grammar) forces the output to match a formal grammar, preventing syntax errors. Option B is wrong because few-shot helps with format but does not enforce grammar. Option C is wrong because temperature changes do not fix syntax. Option D is wrong because fine-tuning is heavy; constrained decoding is a lighter real-time fix.

Answer

Increase the temperature to introduce more varied token choices.

Answer

Fine-tune the model on a large corpus of syntactically correct Python code.

Answer

Provide a few-shot example of correct Python function in the prompt.

Question 11

A law firm uses a generative model to analyze contracts and extract key clauses. The model often outputs irrelevant clauses or misses important ones. They want to improve the relevance of the outputs without retraining the entire model. Which approach is best?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages.. Option C is correct because RAG with a legal corpus retrieves clause-specific paragraphs, and reranking prioritizes relevant content, improving precision. Option A is wrong because temperature adjustment does not improve relevance. Option B is wrong because increasing context length may dilute focus. Option D is wrong because fine-tuning requires significant data and resources.

Answer

Increase the input token limit to provide the entire contract in the prompt.

Answer

Decrease the temperature to make outputs more deterministic.

Answer

Fine-tune the base model on a labeled dataset of contract-clause pairs.

Question 12

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.'. Option D is correct because adding a requirement in the prompt to 'Use formal French' directly instructs the model on the desired style. Option A is wrong because temperature is for creativity, not dialect. Option B is wrong because few-shot with formal examples could help, but it's not a simple modification; a system prompt is simpler. Option C is wrong because fine-tuning is overkill.

Answer

Increase the temperature to encourage more consistent output.

Answer

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

Answer

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

Question 13

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.. Option B is correct because increasing the frequency penalty discourages the model from repeating tokens, directly reducing repetition. Option A is wrong because higher temperature increases randomness but may not specifically target repetition. Option C is wrong because focusing top-p only on a small set may increase repetition. Option D is wrong because decreasing max tokens truncates output but doesn't reduce repetition.

Answer

Decrease the max output tokens to 50.

Answer

Increase the temperature parameter to 1.5.

Answer

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

Question 14

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

Accepted Answer

Apply adversarial debiasing during training or fine-tuning.. Options A and C are correct. A: fine-tuning on a balanced dataset reduces sampling bias in training data. C: adversarial debiasing actively adjusts the model to remove spurious correlations. B is wrong because increasing temperature adds randomness without addressing bias. D is wrong because larger models may amplify biases. E is wrong because reducing max tokens does not affect bias.

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 15

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.. Options A and C are correct. A: prompt engineering with specific instructions can guide the model to be more careful. C: RAG retrieves verified information from external sources, reducing hallucination. B is wrong because increasing top-k introduces randomness. D is wrong because decreasing temperature makes output more deterministic but not necessarily accurate. E is wrong because using a smaller model tends to reduce factual accuracy due to limited knowledge.

Answer

Decrease the temperature to make responses more deterministic.

Answer

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

Answer

Replace the model with a smaller, more focused model.