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| Section | Weight | Objectives |
|---|---|---|
| Design Applications | 14% | - Select appropriate models - Define functional requirements - Design and architect applications |
| Governance | 8% | - Manage permissions and logging - Apply safety filters and guidelines |
| Assembling and Deploying Apps | 22% | - Use Vector Search - Build and deploy RAG pipelines - Package and deploy models |
| Data Preparation | 14% | - Prepare data for RAG applications - Manage data quality and chunking |
| Application Development | 30% | - Integrate Databricks tools (LangChain, etc.) - Develop LLM chains - Implement prompt engineering |
| Evaluation and Monitoring | 12% | - Evaluate model quality and performance - Monitor deployed applications |
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NEW QUESTION # 35
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?
Answer: B
Explanation:
* Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors are confidentiality and sensitivity of the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
* Explanation of Options:
Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements. Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns, BGE-large is assumed to be configurable for secure internal use, making it the optimal choice for this scenario.
NEW QUESTION # 36
A Generative AI Engineer is deploying an agent using Mosaic AI Model Serving. The agent needs to access various Databricks resources, including Vector Search, Databricks SQL, and Functions. They need to find the easiest and best-practice way to authenticate the deployed agent to access these resources.
What approach should they choose?
Answer: D
NEW QUESTION # 37
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?
Answer: A
Explanation:
* Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.
* Explanation of Options:
* Option A: The ability to generate responses in code- This is not relevant to translation quality or safety.
* Option B: The similarity to the previous language- While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.
* Option C: The latency of the response and the length of text generated- These operational metrics are less critical in assessing the qualitative aspects of translation safety.
* Option D: The accuracy and relevance of the responses- This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.
Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.
NEW QUESTION # 38
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?
Answer: A
Explanation:
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
* Option A: Use few shot prompting to instruct the model on expected output format
* Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
* Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").
* Option B: Use zero shot prompting to instruct the model on expected output format
* Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
* Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").
* Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format
* Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
* Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").
* Option D: Use a system prompt to instruct the model to be succinct in its answer
* A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
* Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.
NEW QUESTION # 39
A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal?
Answer: B
Explanation:
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.
NEW QUESTION # 40
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