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

Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.

Explanation of Options:

Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.

Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.

Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.

Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.

OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.

When addressing concerns of hallucination and data leakage in an LLM application for internal company policies, fine-tuning the model on internal data with the hope it learns data boundaries can be problematic:

Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There’s a risk of overfitting to the specific data details, which might lead to unintended leakage.

Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.

Better Approaches:

A,C, andDinvolve setting up operational safeguards and constraints that directly address data leakage and ensure responses are aligned with specific user needs and security levels.

Fine-tuning lacks the targeted control needed for such sensitive applications and can introduce new risks, making it an unsuitable approach in this context.

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.