Pass Databricks-Generative-AI-Engineer-Associate Exam Guide

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.

In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is byincluding a clear instruction in the system prompt(option C).

System Prompt as Guidance:The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on-topic, even if irrelevant content is present in the dataset.

Why This Approach Works:The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.

Why Other Options Are Less Suitable:

A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.

B (Include in the System Prompt about SnoPen AI): This option doesn’t address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.

D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won’t help filter out irrelevant content effectively.

Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant 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.

The task is to choose between LSH and HNSW for a vector database index, prioritizing semantic accuracy. The evaluation must assess how well each method retrieves semantically relevant results. Let’s evaluate the options.

Option A: Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs

Cosine similarity measures semantic closeness between vectors, directly assessing retrieval accuracy in a vector database. Comparing returned results’ embeddings to test inputs’ embeddings evaluates how well LSH or HNSW preserves semantic relationships, aligning with the priority.

Databricks Reference:"Cosine similarity is a standard metric for evaluating vector search accuracy"("Databricks Vector Search Documentation," 2023).

Option B: Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs

BLEU evaluates text generation (e.g., translations), not vector retrieval accuracy. It’s irrelevant for indexing performance.

Databricks Reference:"BLEU applies to generative tasks, not retrieval"("Generative AI Cookbook").

Option C: Compare the Recall-Oriented-Understudy for Gisting Evaluation (ROUGE) scores of returned results for a representative sample of test inputs

ROUGE is for summarization evaluation, not vector search. It doesn’t measure semantic accuracy in retrieval.

Databricks Reference:"ROUGE is unsuited for vector database evaluation"("Building LLM Applications with Databricks").

Option D: Compare the Levenshtein distances of returned results against a representative sample of test inputs

Levenshtein distance measures string edit distance, not semantic similarity in embeddings. It’s inappropriate for vector-based retrieval.

Databricks Reference: No specific support for Levenshtein in vector search contexts.

Conclusion: Option A (cosine similarity) is the correct approach, directly evaluating semantic accuracy in vector retrieval, as recommended by Databricks for Vector Search assessments.