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| Section | Objectives |
|---|---|
| Prompt Engineering | - Prompt design techniques
|
| Foundations of Generative AI | - Large Language Models (LLMs)
|
| Responsible AI and Governance | - AI safety and ethics
|
| Retrieval-Augmented Generation (RAG) | - Vector databases and embeddings
|
| Databricks Mosaic AI Platform | - MLflow for GenAI
|
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NEW QUESTION # 33
A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?
Answer: B
Explanation:
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.
NEW QUESTION # 34
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?
Answer: B
Explanation:
In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing a Safety Guardrail is the appropriate solution for this:
What is a Safety Guardrail?
Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.
Preventing Responses to Political Questions:
The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: "Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." How It Works in Practice:
The LLM system can include a classification layer or trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.
Why Other Options Are Less Suitable:
B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.
C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.
D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.
Therefore, a Safety Guardrail is the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.
NEW QUESTION # 35
A Generative Al Engineer wants their (inetuned LLMs in their prod Databncks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.
What is the most cost-effective and secure option for the Generative Al Engineer to accomplish their gAi?
Answer: A
Explanation:
The goal is to make fine-tuned LLMs from a production (prod) Databricks workspace available for testing in a development (dev) workspace, leveraging Unity Catalog and MLflow, while ensuring cost-effectiveness and security. Let's analyze the options.
* Option A: Use an external model registry which can be accessed from all workspaces
* An external registry adds cost (e.g., hosting fees) and complexity (e.g., integration, security configurations) outside Databricks' native ecosystem, reducing security compared to Unity Catalog's governance.
* Databricks Reference:"Unity Catalog provides a centralized, secure model registry within Databricks"("Unity Catalog Documentation," 2023).
* Option B: Setup a script to export the model from prod and import it to dev
* Export/import scripts require manual effort, storage for model artifacts, and repeated execution, increasing operational cost and risk (e.g., version mismatches, unsecured transfers). It's less efficient than a native solution.
* Databricks Reference: Manual processes are discouraged when Unity Catalog offers built-in sharing:"Avoid redundant workflows with Unity Catalog's cross-workspace access"("MLflow with Unity Catalog").
* Option C: Setup a duplicate training pipeline in dev, so that an identical model is available in dev
* Duplicating the training pipeline doubles compute and storage costs, as it retrains the model from scratch. It's neither cost-effective nor necessary when the prod model can be reused securely.
* Databricks Reference:"Re-running training is resource-intensive; leverage existing models where possible"("Generative AI Engineer Guide").
* Option D: Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model
* Unity Catalog, integrated with MLflow, allows models logged in prod to be centrally managed and accessed across workspaces with fine-grained permissions (e.g., READ for dev). This is cost- effective (no extra infrastructure or retraining) and secure (governed by Databricks' access controls).
* Databricks Reference:"Log models to Unity Catalog via MLflow, then grant access to other workspaces securely"("MLflow Model Registry with Unity Catalog," 2023).
Conclusion: Option D leverages Databricks' native tools (MLflow and Unity Catalog) for a seamless, cost- effective, and secure solution, avoiding external systems, manual scripts, or redundant training.
NEW QUESTION # 36
What is an effective method to preprocess prompts using custom code before sending them to an LLM?
Answer: C
Explanation:
The most effective way to preprocess prompts using custom code is to write a custom model, such as an MLflow PyFunc model. Here's a breakdown of why this is the correct approach:
MLflow PyFunc Models:
MLflow is a widely used platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. A PyFunc model is a generic Python function model that can implement custom logic, which includes preprocessing prompts.
Preprocessing Prompts:
Preprocessing could include various tasks like cleaning up the user input, formatting it according to specific rules, or augmenting it with additional context before passing it to the LLM. Writing this preprocessing as part of a PyFunc model allows the custom code to be managed, tested, and deployed easily.
Modular and Reusable:
By separating the preprocessing logic into a PyFunc model, the system becomes modular, making it easier to maintain and update without needing to modify the core LLM or retrain it.
Why Other Options Are Less Suitable:
A (Modify LLM's Internal Architecture): Directly modifying the LLM's architecture is highly impractical and can disrupt the model's performance. LLMs are typically treated as black-box models for tasks like prompt processing.
B (Avoid Custom Code): While it's true that LLMs haven't been explicitly trained with preprocessed prompts, preprocessing can still improve clarity and alignment with desired input formats without confusing the model.
C (Postprocessing Outputs): While postprocessing the output can be useful, it doesn't address the need for clean and well-formatted inputs, which directly affect the quality of the model's responses.
Thus, using an MLflow PyFunc model allows for flexible and controlled preprocessing of prompts in a scalable way, making it the most effective method.
NEW QUESTION # 37
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
# ... (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?
Answer: B
Explanation:
The error in the original snippet usually stems from the improper instantiation of the LLMChain or the incorrect call to the .generate() method. In LangChain, an LLMChain requires two primary components: an LLM (the engine) and a Prompt (the template). Option C provides the correct syntax: first, the PromptTemplate is defined with the correct input_variables. Second, the OpenAI model is instantiated. Third, the LLMChain binds the model and the prompt together. Finally, the .generate() method expects a list of dictionaries, where each dictionary represents a set of inputs for the prompt variables. Options A, B, and D in the original image contain syntax errors such as passing the variable directly into the chain initialization or missing the dictionary list format required by the standard LangChain API for batch-like generation.
NEW QUESTION # 38
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