Databricks-Generative-AI-Engineer-Associateテストサンプル問題 & Databricks-Generative-AI-Engineer-Associate入門知識

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Databricks Databricks-Generative-AI-Engineer-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
トピック 2	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.
トピック 3	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
トピック 4	Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.

>> Databricks-Generative-AI-Engineer-Associateテストサンプル問題 <<

Databricks-Generative-AI-Engineer-Associate入門知識、Databricks-Generative-AI-Engineer-Associate参考書内容

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Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q64-Q69):

質問 # 64
A Generative AI Engineer is building a compound AI system for an organization. The goal is to automate the processing of incoming customer event reports against a coding system and corporate-guidelines documentation. The system must handle three distinct user-request types: answering questions from guidelines documents, extracting specific event codes from reviewers' notes, and routing ambiguous requests to the appropriate specialized handler. All three capabilities must operate under a single entry point that interprets user intent and delegates accordingly.
Which Agent Brick should serve as the top-level orchestrator in this architecture?

A. Multi-Agent Supervisor, because it interprets incoming requests and delegates tasks to specialized sub- agents.
B. Knowledge Assistant, because the chatbot interface can handle multi-turn conversations.
C. Knowledge Assistant, because it can be configured with multiple vector indexes to handle all three request types simultaneously.
D. Multi-Agent Supervisor, because it can be used without Knowledge Assistant and Information Extraction agents.

正解：A

解説：
The Multi-Agent Supervisor is designed to coordinate specialized agents and tools behind a unified entry point. It interprets the incoming request, selects suitable capabilities, delegates work, and combines results when necessary. In this scenario, document questions can be handled by a knowledge-retrieval specialist, while event-code extraction can be assigned to a specialized extraction capability. The supervisor provides the coordination needed to select between those tasks and handle requests requiring multiple steps. A Knowledge Assistant's conversational interface does not itself make it the appropriate general-purpose orchestrator.
Adding vector indexes also does not establish specialized extraction and routing behavior. Although option A names the same component, its justification misses the central requirement: coordinated delegation across distinct capabilities. Databricks documentation

質問 # 65
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?

A. Use a neutralizer to normalize the tone and style of the underlying documents
B. Fine-tune the LLM on a dataset of desired tone and style
C. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
D. Include few-shot examples in the prompt to the LLM

正解：A

解説：
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.

質問 # 66
Databricks offers a number of built-in AI judges that provide metrics and rationale for different types of quality issues a Generative AI application may have.
Which of the following pairs of judges both require a ground-truth label in the evaluation dataset field expected_response to execute?

A. correctness, groundedness.
B. guideline_adherence, chunk_relevance.
C. relevance_to_query, chunk_relevance.
D. context_sufficiency, correctness.

正解：D

解説：
In the Agent Evaluation terminology used by this question, correctness compares the generated response with the expected answer, while context_sufficiency determines whether the retrieved context contains enough information to produce that expected answer. Both therefore depend on ground-truth information supplied through expected_response. Groundedness instead examines whether the response is supported by the retrieved context; it does not inherently need a reference answer. Chunk relevance and relevance to the query assess alignment with the user's request, while guideline adherence evaluates compliance with specified instructions. Databricks explicitly distinguishes the ground-truth-dependent judges in its migration documentation. In MLflow 3, names and schemas have evolved, including expectations.expected_response and explicitly selected scorers, so the question should be interpreted using its stated legacy judge terminology.
Databricks documentation

質問 # 67
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.)

A. Reduce the maximum output tokens of the new model
B. Retrain the response generating model using ALiBi
C. Use a smaller embedding model to generate
D. Decrease the chunk size of embedded documents
E. Reduce the number of records retrieved from the vector database

正解：D、E

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.
OptionsCandDare 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 # 68
A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. Thematch should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?

A. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.
B. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.
C. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
D. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

正解：D

質問 # 69
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