Databricks-Generative-AI-Engineer-Associate Databricks Certified Generative AI Engineer Associate Questions and Answers

Problem Context: The Generative AI Engineer needs a tool to build amulti-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.

Explanation of Options:

Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.

Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.

Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.

Option D: LangChain: LangChain is a purpose-built framework designed specifically fororchestrating multi-step workflowswith large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.

Thus,LangChainis the most suitable library for creating multi-step LLM-based workflows.

To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:

Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.

Correct Use of Classes and Methods:

The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.

The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.

The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.

Why Other Options Are Incorrect:

Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.

Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.

Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.

Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.

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.

 Context: The goal is to improve the online customer experience in a restaurant by handling common inquiries about bookings, minimizing escalations, and maintaining personalized interactions.

 Explanation of Options:

Option A: Grouping and summarizing chat logs by user could provide insights into customer interactions but does not directly address the task of handling booking inquiries or minimizing escalations.

Option B: Using chat logs to generate interactive buttons for booking details directly supports the goal of facilitating online bookings, minimizing the need for human intervention by providing clear, interactive options for customers to self-serve.

Option C: Classifying sentiment of customer reviews does not directly help with booking inquiries, although it might provide valuable feedback insights.

Option D: Providing cancellation options is helpful but narrowly focuses on one aspect of the booking process and doesn't support the broader goal of handling common inquiries about bookings.

Option Bbest supports the goal of improving online interactions by using chat logs to generate actionable items for customers, helping them complete booking tasks efficiently and reducing the need for human intervention.

To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:

Chunking and Vector Store Embedding:HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into avector store(a database that stores vector representations of text). Each chunk of text is transformed into anembeddingusing a transformer-based model, which allows for efficient similarity-based retrieval.

Using Vector Search for Retrieval:When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.

LLM to Generate a Response:Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.

Why Other Options Are Less Suitable:

A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.

B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.

C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it’s focused on collaborative filtering rather than text-based retrieval.

Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.

In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here’s why option A is the most appropriate solution:

Context Relevance:The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.

Vector Search and Embedding Similarity:RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing thesemantic similarity searchprocess ensures that the closest matches are actually relevant to the query.

Fine-tuning the Retrieval Process:By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.

Why Other Options Are Less Suitable:

B (Caching FAQs): Caching can speed up responses for frequently asked questions but won’t improve the relevance of the retrieved content for less frequent or new queries.

C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.

D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.

Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.

To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let’s go through the correct sequence as depicted in the diagram and explained in option A:

Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.

Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.

Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.

Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response-generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.

Why Other Options Are Less Suitable:

B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.

Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response-generating LLM, which is option A.

In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here’s why:

Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherentlyimprove therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.

What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model’s responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM’s responses to the financial domain and avoid irrelevant or harmful outputs.

Why Other Options Are More Relevant:

A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.

C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.

D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM’s output ensures the final answers are aligned with financial content expectations.

Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.

For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here’s the reasoning:

Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks. This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.

Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.

Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.

Why Other Options Are Less Suitable:

A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine-tuned for code generation as CodeLlama.

B (BGE-large): This model may not specifically focus on code generation.

C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.

Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.

Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.

Explanation of Options:

Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.

Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow’s metrics for a structured and standardized assessment.

Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system’s components but rather on comparing different models.

Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.

OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.