Databricks Databricks-Generative-AI-Engineer-Associate Exam With Confidence Using Practice Dumps

Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.

Explanation of Options:

Option A: Use a smaller embedding model to generate– This wouldn't necessarily address the issue of prompt size exceeding the model’s token limit.

Option B: Reduce the maximum output tokens of the new model– This option affects the output length, not the size of the input being too large.

Option C: Decrease the chunk size of embedded documents– This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.

Option D: Reduce the number of records retrieved from the vector database– By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.

Option E: Retrain the response generating model using ALiBi– Retraining the model is contrary to the stipulation not to change the response generating model.

OptionsCandDare the most effective solutions to manage the model’s shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

 Problem Context: The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.

 Explanation of Options:

Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing to frequently retrain the model. This method offers a balance of cost-effectiveness and functionality.

Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.

Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it’s part of an ongoing retraining or updating mechanism, which would increase costs.

Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.

Option Ais the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.

Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.

Explanation of Options:

Option A: The ability to generate responses in code– This is not relevant to translation quality or safety.

Option B: The similarity to the previous language– While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.

Option C: The latency of the response and the length of text generated– These operational metrics are less critical in assessing the qualitative aspects of translation safety.

Option D: The accuracy and relevance of the responses– This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.

Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.