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NEW QUESTION # 64
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?
Answer: C
Explanation:
In this case, the Generative AI Engineer is developing an application to generate personalized birthday poems, but there's a need to safeguard against malicious user inputs. The best solution is to implement a safety filter (option A) to detect harmful or inappropriate inputs.
Safety Filter Implementation:
Safety filters are essential for screening user input and preventing inappropriate content from being processed by the LLM. These filters can scan inputs for harmful language, offensive terms, or malicious content and intervene before the prompt is passed to the LLM.
Graceful Handling of Harmful Inputs:
Once the safety filter detects harmful content, the system can provide a message to the user, such as "I'm unable to assist with this request," instead of processing or responding to malicious input. This protects the system from generating harmful content and ensures a controlled interaction environment.
Why Other Options Are Less Suitable:
B (Reduce Interaction Time): Reducing the interaction time won't prevent malicious inputs from being entered.
C (Continue the Conversation): While it's possible to acknowledge malicious input, it is not safe to continue the conversation with harmful content. This could lead to legal or reputational risks.
D (Increase Compute Power): Adding more compute doesn't address the issue of harmful content and would only speed up processing without resolving safety concerns.
Therefore, implementing a safety filter that blocks harmful inputs is the most effective technique for safeguarding the application.
NEW QUESTION # 65
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
Answer: A,B
Explanation:
* 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.
Options C and D are 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.
NEW QUESTION # 66
A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn't hallucinate or leak confidential data.
Which approach should NOT be used to mitigate hallucination or confidential data leakage?
Answer: D
Explanation:
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.
NEW QUESTION # 67
A Generative AI Engineer at a legal firm is designing a RAG system to analyze historical legal cases. The system needs to process millions of court opinions and legal documents, already organized by time and topic, to track how interpretations of specific laws have evolved over time. All of these documents are in plain-text. The engineer needs to choose a chunking method that would most effectively preserve continuity and the temporal nature of the cases. Which method do they choose?
Answer: B
Explanation:
In the context of legal document analysis where the "evolution of interpretation" is the primary goal, preserving narrative continuity is paramount. Windowed summarization with overlapping chunks is the most effective method for this use case. Overlapping (e.g., 10-15% of the chunk size) ensures that sentences or concepts split at the boundary of one chunk are preserved in the next, preventing the loss of critical context that often occurs in legal jargon. Furthermore, windowed summarization allows the system to condense long-form court opinions into manageable parts while maintaining the chronological "thread" of the argument. While sentence-level embeddings with metadata (D) are useful for filtering, they often lack the sufficient context required to understand the nuances of a legal ruling. A windowed approach provides the LLM with enough surrounding text to understand the "why" behind a legal evolution, rather than just the "when."
NEW QUESTION # 68
A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios Which authentication method should they choose?
Answer: D
Explanation:
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.
NEW QUESTION # 69
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