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| Section | Objectives |
|---|---|
| Prompt Engineering | - Prompt design techniques
|
| Retrieval-Augmented Generation (RAG) | - RAG architecture
|
| Databricks Mosaic AI Platform | - Model development and serving
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| Responsible AI and Governance | - AI safety and ethics
|
| Foundations of Generative AI | - Large Language Models (LLMs)
|
>> Databricks-Generative-AI-Engineer-Associate日本語版参考資料 <<
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質問 # 66
A Generative Al Engineer is building a production-ready LLM system which replies directly to customers.
The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?
正解：C
解説：
The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let's assess the options.
* Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses
* Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.
* Databricks Reference:"Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently"("Foundation Model API Documentation," 2023).
* Option B: Add some LLM calls to their chain to detect unsafe content before returning text
* Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.
* Databricks Reference:"Ad-hoc LLM checks are less efficient than purpose-built safety solutions" ("Building LLM Applications with Databricks").
* Option C: Add a regex expression on inputs and outputs to detect unsafe responses
* Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.
* Databricks Reference:"Regex-based filtering is limited for complex safety needs"("Generative AI Cookbook").
* Option D: Ask users to report unsafe responses
* User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn't limit unsafe outputs proactively and requires additional effort for feedback handling.
* Databricks Reference:"Proactive guardrails are preferred over user-driven monitoring" ("Databricks Generative AI Engineer Guide").
Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks' infrastructure for seamless safety integration.
質問 # 67
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?
正解：C
解説：
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.
質問 # 68
A Generative Al Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
正解：D
解説：
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent'stool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference:"Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems"("Databricks Feature Engineering Guide," 2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference:"Direct SQL queries are flexible but may introduce overhead in real-time applications"("Building LLM Applications with Databricks").
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference:"Vector search excels for unstructured data, not structured tabular lookups"("Databricks Vector Search Documentation").
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference:"Avoid external systems when Delta tables provide real-time data natively"("Databricks Workflows Guide").
Conclusion: Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
質問 # 69
An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?
正解：B、D
解説：
To optimize for code generation performance and hosting costs, a Generative AI engineer must look at specific metrics.
Big Code Models Leaderboard (A): This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.
Number of model parameters (B): This is the primary driver of hosting costs. Larger models (e.g., 70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.
Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.
質問 # 70
A Generative AI Engineer is managing prompt templates using MLflow v3.x for a document summarization pipeline. A regulatory audit requires the team to demonstrate exactly which prompt version was used to generate outputs on a specific date three months ago, including the exact prompt text and any variables used at that time.
Which combination of MLflow v3.x capabilities allows the engineer to satisfy this audit requirement?
正解：C
解説：
The Prompt Registry preserves identifiable prompt versions, allowing the team to recover the exact template associated with a historical execution. Linking inference activity to the prompt's name and version establishes which registered template the application actually used. MLflow also supports lineage between prompt versions, application versions, and execution traces. However, satisfying the entire audit requires retaining the actual runtime variable values or the fully rendered prompt in the relevant execution records. Registry history alone preserves the template, not every value substituted into it. Therefore, D is the best option, provided the logging captures those inputs. Model webhooks, experiment tags, or table history alone do not provide the same explicit prompt-version lineage and complete historical reconstruction. Databricks documentation
質問 # 71
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