20+ practice questions focused on Techniques to Improve Generative AI Model Output — one of the most tested topics on the Google Cloud Generative AI Leader Generative AI Leader exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Techniques to Improve Generative AI Model Output PracticeA company uses a text-to-image model to generate marketing visuals. The outputs often contain distorted human faces. Which technique is most likely to improve face generation?
Explanation: Fine-tuning the model on a high-quality dataset of human faces directly addresses the distortion issue by specializing the model for face generation. Option B (increasing output resolution) may improve overall image sharpness but does not specifically correct face distortions. Option C (increasing inference steps) can enhance image coherence but is not targeted at face quality. Option D (reducing classifier-free guidance scale) decreases prompt adherence, which could actually worsen face generation rather than improve it.
A team is deploying a large language model for legal document summarization. They find the model occasionally omits critical legal clauses. Which improvement technique would be most effective?
Explanation: Prompt engineering with explicit instructions to include all required sections directly addresses omissions by guiding the model's output structure. Option B (increasing top_p to 1.0) increases randomness and may worsen omissions. Option C (fine-tuning on legal summaries) requires substantial labeled data and may not directly enforce clause completeness. Option D (lowering temperature to 0.1) reduces randomness but does not ensure all required sections are included.
A team notices their text generation model repeats phrases excessively. Which technique would most directly reduce repetition?
Explanation: Applying a repetition penalty directly penalizes the model for generating tokens that have already appeared in the output, reducing the probability of repeated phrases. Unlike sampling or beam search adjustments, this technique explicitly targets the repetition issue by scaling down the logits of previously generated tokens during decoding.
Refer to the exhibit. The team changed the generation parameters to reduce output variability. However, summaries now often repeat the same phrases. Which parameter change is most likely causing the repetition?
Explanation: Reducing temperature to 0.2 makes the model highly deterministic, causing it to repeatedly select the most likely tokens and thus produce repetitive phrases. Option A (reducing top_p) narrows token selection to a cumulative probability threshold, which can also reduce variability but is less direct in causing repetition. Option C (using the same model) does not affect randomness. Option D (reducing top_k) limits the number of top tokens considered, which reduces diversity but is not the primary cause of repetition here.
A media company is using Vertex AI's Imagen model to generate images for marketing campaigns. They have a set of prompts that describe desired scenes, but the generated images often contain artifacts such as distorted faces or unnatural lighting. The team has tried varying the prompt wording but the issues persist. They are using the default parameters (no modifications). They have a budget for additional compute resources and want to improve image quality without switching to a more expensive model. The team has access to a small set of high-quality images in the same style as their target outputs. What should the team do?
Explanation: Fine-tuning the Imagen model on the small set of high-quality images allows the model to learn the desired style and reduce artifacts like distorted faces and unnatural lighting, improving output quality without switching to a more expensive model. Option A is incorrect because increasing the guidance scale may cause the model to overfit to the prompt and potentially introduce more artifacts rather than fix them, and the team already has issues with prompt adherence. Option B is incorrect because using more detailed prompts with negative prompts might help but the team already tried varying wording without success; the root cause is the model's lack of specific training on the desired quality, which fine-tuning directly addresses. Option D is incorrect because generating more images per prompt does not improve the per-image quality; it only increases the chance of finding a good one, and the team wants to improve overall image quality.
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