Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 4 — 15 Questions

Question 1

A chatbot built with Vertex AI PaLM API often provides outdated information about company policies because the training data is months old.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 2

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 3

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 4

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 5

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 6

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.. Providing few-shot examples of correctly labeled sentiment in the prompt directly guides the model to understand the desired output format and classification boundaries through in-context learning. This technique leverages the model's ability to generalize from a small number of labeled examples without requiring retraining, improving accuracy for specific sentiment extraction tasks.

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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 7

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 domain, ensuring that legal terminology and clause structures are translated with high fidelity. This supervised learning approach directly aligns the model's output with ground-truth translations, preserving original meaning by learning the precise mapping between source and target legal texts.

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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 8

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 9

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 10

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.. Safety filters in generative AI models are triggered by content that violates predefined policies, often due to ambiguous or overly broad system instructions. By refining the system instructions to explicitly frame the medical context (e.g., 'This is a clinical discussion for educational purposes'), the developer can reduce false positives. Additionally, using a model endpoint fine-tuned for medical domains (e.g., Med-PaLM 2) bypasses generic safety restrictions while maintaining compliance with ethical guidelines.

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Disable the safety filter entirely to allow all topics.

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

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Increase the input token limit to 2000.

Question 11

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.. Using a system prompt with explicit instructions to detect all sentiments, including negative, guides the model to consider the full emotional range. Option B is wrong because fine-tuning on a balanced dataset is a good practice but is not the quickest fix; prompt engineering is more immediate. Option C is wrong because reducing max tokens only affects output length, not sentiment bias. Option D is wrong because increasing temperature adds randomness and does not enforce balance.

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

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

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Increase the temperature parameter to reduce model confidence.

Question 12

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 13

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 14

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.

Question 15

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.

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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.