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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45-60 |
| Exam Format: | Multiple select, Multiple choice |
| Exam Price: | USD 200 |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Exam Duration: | 90 minutes |
| Recommended Training: | Databricks Academy - Generative AI Courses Mosaic AI Documentation |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
>> Databricks-Generative-AI-Engineer-Associate資格難易度 <<
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質問 # 80
A Generative AI Engineer has been reviewing issues with their company's LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?
正解：C
解説：
Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity. Instead of sending a single, massive, and highly complex prompt to an LLM-which often results in reasoning errors or hallucinations-chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to "identify the legal jurisdiction," followed by a step to "extract relevant statutes," and finally a step to "summarize the findings." This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output. It also allows for intermediate validation and error handling between steps, which is impossible in a single-call architecture.
質問 # 81
A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios Which authentication method should they choose?
正解：A
解説：
The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let's evaluate the options based on Databricks' security guidelines for production scenarios.
Option A: Use an access token belonging to service principals
Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.
Databricks Reference: "For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials" ("Databricks Security Best Practices," 2023). Additionally, the "Foundation Model API Documentation" states: "Service principal tokens are recommended for programmatic access to Foundation Model APIs." Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.
Databricks Reference: "While token rotation is a good practice, service principals are preferred over user accounts for application authentication" ("Managing Tokens in Databricks," 2023).
Option C: Use OAuth machine-to-machine authentication
OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks' Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.
Databricks Reference: "OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads" ("Databricks Authentication Guide," 2023).
Option D: Use an access token belonging to any workspace user
Using a user's access token ties the application to an individual's identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it's not scalable or auditable for production.
Databricks Reference: "Avoid using personal user tokens for production applications due to security and governance concerns" ("Databricks Security Best Practices," 2023).
Conclusion: Option A is the best choice, as it uses a service principal's access token, aligning with Databricks' security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.
質問 # 82
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
正解：A、B
解説：
* Problem Context : After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options :
* Option A: Use a smaller embedding model to generate - This wouldn ' t necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model - This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents - This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model ' s context length limitations.
* Option D: Reduce the number of records retrieved from the vector database - By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi - Retraining the model is contrary to the stipulation not to change the response generating model.
Options C and D are the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.
質問 # 83
A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have:
A catalog with hundreds of thousands of cars manufactured since the 1960s Historical searches with user queries and successful matches Descriptions of their own cars in multiple languages They have already selected an open-source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?
正解：D
解説：
According to Generative AI engineering standards for Retrieval-Augmented Generation (RAG), chunking strategy is a critical optimization variable. Setting the chunk size to match the model's maximum context window (e.g., 4k or 8k tokens) is a poor practice and should be discarded. Large chunks introduce significant "noise" into the LLM's context, as only a small portion of a massive chunk usually contains the answer to a specific query. This leads to the "lost in the middle" phenomenon where LLMs struggle to extract relevant information from bloated contexts. Furthermore, large chunks reduce the precision of the vector search. Standard best practices involve using smaller, semantically meaningful chunks (typically 256-512 tokens) with overlap to maintain context. In contrast, metadata filtering (B) is essential for narrowing searches to specific car years, fine-tuning embeddings (C) improves retrieval accuracy for domain-specific technical terms, and few-shot examples (D) guide the LLM's output format and tone.
質問 # 84
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?
正解：D、E
解説：
Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks' Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks' MosaicML integrations. Let's evaluate the required components:
* Understanding the Requirements :
* Conversational capabilities : The app must generate natural, coherent responses.
* Knowledge retrieval : It must access external or domain-specific knowledge.
* Contextual memory : It must remember prior interactions in the conversation.
* Databricks Reference: " A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge " ( " Databricks Generative AI Cookbook, " 2023).
* Evaluating the Options :
* A. (Q) : This appears incomplete or unclear (possibly a typo). Without further context, it's not a valid component.
* B. Vector Stores : These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.
* Databricks Reference : " Vector stores, such as those integrated with Databricks' Lakehouse, enable efficient retrieval of contextual data for LLMs " ( " Building LLM Applications with Databricks " ).
* C. Conversation Buffer Memory : This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It's essential for contextual memory.
* Databricks Reference : " Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses " ( " Generative AI Engineer Guide " ).
* D. External tools : These (e.g., APIs or calculators) enhance functionality but aren't required for a basic chat app with the specified capabilities.
* E. Chat loaders : These might refer to data loaders for chat logs, but they're not a core chain component for conversational functionality or memory.
* F. React Components : These relate to front-end UI development, not the LLM chain's backend functionality.
* Selecting the Two Required Components :
* For knowledge retrieval , Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* For contextual memory , Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.
* While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.
Conclusion : The two required chain components are B. Vector Stores and C. Conversation Buffer Memory
, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks' documented best practices for LLM-enabled chat applications.
質問 # 85
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