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| Section | Objectives |
|---|---|
| Topic 1: Foundations of Generative AI | - Large Language Models (LLMs)
|
| Topic 2: Retrieval-Augmented Generation (RAG) | - Vector databases and embeddings
|
| Topic 3: Prompt Engineering | - Prompt evaluation
|
| Topic 4: Responsible AI and Governance | - AI safety and ethics
|
| Topic 5: Databricks Mosaic AI Platform | - Model development and serving
|
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NEW QUESTION # 88
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?
Answer: B
Explanation:
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is byincluding a clear instruction in the system prompt(option C).
* System Prompt as Guidance:The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on- topic, even if irrelevant content is present in the dataset.
* Why This Approach Works:The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
* Why Other Options Are Less Suitable:
* A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
* B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
* D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.
NEW QUESTION # 89
All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?
Answer: A
Explanation:
When working within a Databricks notebook, several high-level SDKs are "Databricks-aware." The MLflow Deployments SDK (C) and the Databricks Python SDK (D) are designed to automatically look for the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables provided by the notebook context. The OpenAI client (A), when configured for Databricks via Mosaic AI Gateway, also typically handles authentication via workspace integration in recent versions. However, the REST API via the requests library (B) is a generic Python HTTP client. It has no intrinsic knowledge of the Databricks environment. To use it, an engineer must manually extract the token (e.g., via dbutils.notebook.entry_point...) and explicitly pass it in the Authorization: Bearer <token> header of the request. Without this manual step, the requests library will fail with a 401 Unauthorized error.
NEW QUESTION # 90
A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.
Which approach will do this?
Answer: C
Explanation:
The task requires an LLM pipeline for multi-stage reasoning with external tools, necessitating planning, adaptability, and complex reasoning. Let's evaluate the options based on Databricks' recommendations for advanced LLM workflows.
Option A: Train the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge This approach limits the LLM to its static knowledge base, excluding external tools and multi-stage reasoning. It can't adapt or plan actions dynamically, failing the requirements.
Databricks Reference: "External tools enhance LLM capabilities beyond pre-trained knowledge" ("Building LLM Applications with Databricks," 2023).
Option B: Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary ReAct (Reasoning + Acting) combines reasoning traces (step-by-step logic) with actions (e.g., tool calls), enabling the LLM to plan, adapt, and execute complex tasks iteratively. This meets all requirements: multi-stage reasoning, tool use, and adaptability.
Databricks Reference: "Frameworks like ReAct enable LLMs to interleave reasoning and external tool interactions for complex problem-solving" ("Generative AI Cookbook," 2023).
Option C: Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously Unstructured, spontaneous API calls lack planning and may lead to inefficient or incorrect tool usage. This doesn't ensure effective multi-stage reasoning or adaptability.
Databricks Reference: Structured frameworks are preferred: "Ad-hoc tool calls can reduce reliability in complex tasks" ("Building LLM-Powered Applications").
Option D: Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer CoT improves reasoning but relies on manual tool interaction, breaking automation and adaptability. It's not a scalable pipeline solution.
Databricks Reference: "Manual intervention is impractical for production LLM pipelines" ("Databricks Generative AI Engineer Guide").
Conclusion: Option B (ReAct) is the best approach, as it integrates reasoning and tool use in a structured, adaptive framework, aligning with Databricks' guidance for complex LLM workflows.
NEW QUESTION # 91
A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?
Answer: C
Explanation:
The task at hand is to improve the LLM's ability to generate poem-like article summaries with the desired tone and style. Using aneutralizerto normalize the tone and style of the underlying documents (option B) will not help improve the LLM's ability to generate the desired poetic style. Here's why:
* Neutralizing Underlying Documents:A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with aspecific tone and style(like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.
* Why Other Options Improve Results:
* A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.
* C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.
* D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model's ability to generate outputs that match the target format.
Therefore, using a neutralizer (option B) isnotan effective method for achieving the goal of generating stylized poetic summaries.
NEW QUESTION # 92
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: A
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 # 93
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