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

Question 1

A data scientist is using a large language model to generate product descriptions. The descriptions are often too verbose. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty.. Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or ideas, which directly addresses verbosity by discouraging repetitive or overly detailed descriptions. This parameter penalizes tokens that have already appeared in the generated text, promoting more concise and varied output. Other adjustments like temperature or top-k affect randomness and diversity but do not specifically target repetition or length.

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Decrease the top-k value.

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Increase the max output tokens.

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Decrease the temperature.

Question 2

A chatbot built with Vertex AI PaLM API often provides outdated information about company policies because the training data is months old.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?

Accepted Answer

Implement grounding by connecting to a knowledge base of current policies.. 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.

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Use prompt engineering to instruct the model to say 'I don't know' if unsure.

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Increase the context window to include more history.

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Fine-tune the model on the latest policy documents.

Question 3

A research team is using a large language model to analyze medical research papers and generate summaries. They need to minimize hallucinations while retaining key details. They have access to a curated database of paper abstracts. Which approach is best?

Accepted Answer

Implement RAG to retrieve relevant abstracts and incorporate them into the prompt.. Retrieval-Augmented Generation (RAG) directly addresses hallucination by grounding the model's output in a curated database of paper abstracts. By retrieving relevant abstracts and injecting them into the prompt, the model generates summaries based on verified facts rather than relying solely on its parametric knowledge, which is the most effective way to minimize hallucinations while retaining key details.

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Fine-tune the model on the entire database of papers.

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Use chain-of-thought prompting to reason step-by-step.

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Use few-shot prompting with examples of accurate summaries and set temperature=0.0.

Question 4

A developer is using the Gemini API to generate creative marketing copy. They want the output to be more diverse and unexpected. Which parameter should they increase?

Accepted Answer

Temperature.. Increasing the temperature parameter makes the model's output probabilities more uniform, encouraging it to sample less likely tokens and produce more diverse, unexpected, and creative text. A higher temperature (e.g., >1.0) flattens the probability distribution, so the model is more likely to choose surprising word combinations rather than the most probable ones.

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Presence penalty.

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Top-p.

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Frequency penalty.

Question 5

After deploying a text-to-image model, the output images often contain distorted objects. The team suspects the prompt is too complex. Which prompt engineering technique should they try first?

Accepted Answer

Break the prompt into simpler, separate steps.. Breaking a complex prompt into simpler, separate steps reduces the cognitive load on the diffusion model, allowing it to focus on generating each element sequentially. This technique, often called 'prompt decomposition' or 'step-by-step prompting,' directly addresses the root cause of distorted objects when the model struggles to attend to multiple conflicting details simultaneously in a single pass.

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Increase the guidance scale.

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Add more descriptive adjectives.

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Use a negative prompt to exclude distortions.

Question 6

A company is using a fine-tuned LLM for generating financial reports. They need to ensure that the output complies with regulatory standards and does not include speculative content. Which combination of techniques should they implement?

Accepted Answer

Fine-tune the model on historical compliant reports, use RAG with a regulatory database, and implement a human-in-the-loop review.. Fine-tuning on historical compliant reports ensures the model learns from past regulatory requirements, RAG with a regulatory database provides up-to-date compliance information, and human-in-the-loop review adds a final verification layer to catch any non-compliant or speculative content.

Option A (safety settings, low top-p, limit tokens) may reduce harmful content but does not guarantee regulatory compliance. Option C (larger model) alone does not enforce specific regulations. Option D (system instruction, temperature 0.0, keyword filter) is insufficient for complex regulatory standards.

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Increase the model's safety settings to maximum, use a low top-p value, and limit output tokens.

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Use a larger model with more parameters and rely on its inherent knowledge.

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Use a system instruction to adhere to regulations, set temperature to 0.0, and apply a keyword filter.

Question 7

A team is using a language model for customer feedback analysis. They want to improve the accuracy of sentiment extraction. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Provide few-shot examples of correctly labeled sentiment in the prompt.. Two complementary techniques improve sentiment extraction accuracy. First, providing few-shot examples of correctly labeled sentiment in the prompt guides the model through in-context learning, clarifying the desired output format and classification boundaries without retraining. Second, fine-tuning the model on a labeled dataset of customer feedback adapts the model's weights to the specific domain and label distribution, yielding more accurate and consistent sentiment classification than prompting alone. Increasing temperature, using a generic built-in sentiment API, or enforcing JSON output format do not directly improve classification accuracy.

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Increase the temperature to 0.8 to allow more creative interpretations.

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Use the model's built-in sentiment analysis API instead of prompting.

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Add a system instruction that asks the model to strictly follow JSON output format.

Question 8

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.. Implementing a safety filter that blocks outputs containing harmful medical terminology directly mitigates the risk of generating dangerous advice. This acts as a post-processing guardrail, intercepting model outputs that include terms associated with diagnoses, dosages, or procedures that could lead to patient harm. It is a standard practice in high-stakes domains to layer such filters on top of the generative model.

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

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

Question 9

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.. Fine-tuning on a parallel corpus of legal translations adapts the model's weights to the specific legal domain, improving fidelity for legal terminology and clause structures by learning from ground-truth translations. In addition, providing a glossary of key legal terms with their approved translations constrains the model to use consistent, correct terminology, reducing meaning-altering substitutions. Together, these two approaches most effectively preserve the original meaning. Prompt engineering alone (Option A) and parameter tweaks such as top-p or higher temperature (Options B and E) do not address the root cause of semantic drift.

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Provide the full contract context in a single prompt.

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

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Increase the temperature to allow more creative phrasing.

Question 10

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.. Grounding with a knowledge base is the correct approach because it anchors the model's output to a trusted, external source of facts, directly improving factual accuracy without modifying the model's weights. This technique uses retrieval-augmented generation (RAG) to fetch relevant documents from the knowledge base and inject them into the prompt context, ensuring the summary is based on verified information rather than relying solely on the model's parametric memory.

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Enable the support engine.

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Increase the model's context window.

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Re-train the model with a dataset of facts.

Question 11

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.. Temperature controls the randomness of token sampling. Lowering temperature (e.g., to 0.2) makes the model's output more deterministic by reducing the probability of low-likelihood tokens, thus decreasing variability across calls for the same prompt.

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Decrease topK to 10.

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Increase candidateCount to 3.

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Increase maxOutputTokens to 2000.

Question 12

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.. Grounding with the company's product knowledge base (D) is the most effective technique to improve factual accuracy. Grounding involves retrieving relevant information from a trusted knowledge source and providing it to the model as context, so the model generates answers based on that data rather than relying solely on its training. This reduces hallucinations and ensures responses are accurate and up-to-date.

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

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Use a higher temperature setting to encourage more creative responses.

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

Question 13

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.. 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 diverse beam search generates varied outputs but does not inherently improve specificity; it may produce multiple candidates but does not ensure clinical relevance. Option C is wrong because top-k sampling can reduce the vocabulary but does not guarantee medical accuracy or detail. Option D is wrong because increasing temperature adds randomness, which may produce longer or more varied captions but often introduces irrelevant words rather than specific clinical terms.

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

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

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

Question 14

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.. Constrained decoding (also called grammar-guided generation) enforces the syntax rules of the target language (e.g., Python) during token generation by restricting the model's output to only valid tokens according to a formal grammar (e.g., EBNF or context-free grammar). This directly prevents syntax errors like incorrect braces or indentation, which are structural, not semantic, issues. Techniques such as using a parser-based logit processor or a constrained beam search ensure every generated token sequence is syntactically valid.

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Increase the temperature to introduce more varied token choices.

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

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

Question 15

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.. Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker is the best approach because it grounds the model's outputs in a trusted, external knowledge base, ensuring that only the most relevant clauses are retrieved and used for generation. This directly addresses the problem of irrelevant outputs and missed clauses without requiring retraining, as the model can dynamically fetch and rank the most on-topic passages from the curated database.

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

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Decrease the temperature to make outputs more deterministic.

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