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

When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's whyDis the best choice:

Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.

Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options. This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.

Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.

This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency-sensitive, cost-conscious application.

The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let’s evaluate the options based on Databricks’ security guidelines for production scenarios.

Option A: Use an access token belonging to service principals

Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.

Databricks Reference:"For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials"("Databricks Security Best Practices," 2023). Additionally, the "Foundation Model API Documentation" states:"Service principal tokens are recommended for programmatic access to Foundation Model APIs."

Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal

Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.

Databricks Reference:"While token rotation is a good practice, service principals are preferred over user accounts for application authentication"("Managing Tokens in Databricks," 2023).

Option C: Use OAuth machine-to-machine authentication

OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks’ Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.

Databricks Reference:"OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads"("Databricks Authentication Guide," 2023).

Option D: Use an access token belonging to any workspace user

Using a user’s access token ties the application to an individual’s identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it’s not scalable or auditable for production.

Databricks Reference:"Avoid using personal user tokens for production applications due to security and governance concerns"("Databricks Security Best Practices," 2023).

Conclusion: Option A is the best choice, as it uses a service principal’s access token, aligning with Databricks’ security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.

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.