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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 3	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.
Topic 4	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q82-Q87):

NEW QUESTION # 82
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. Fine-tune the LLM on a dataset of desired tone and style
B. Use a neutralizer to normalize the tone and style of the underlying documents
C. Include few-shot examples in the prompt to the LLM
D. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style

Answer: B

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 # 83
A company has a typical RAG-enabled, customer-facing chatbot on its website.

Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

A. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
B. 1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM
C. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model
D. 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM

Answer: D

Explanation:
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
* Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
* Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
* Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
* Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response- generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
* B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response- generating LLM, which is option A.

NEW QUESTION # 84
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?

A. Implementing metadata filtering based on car models and years
B. Setting chunk size to match the model ' s context window to maximize coverage
C. Fine-tuning an embedding model on automotive terminology
D. Adding few-shot examples for response generation

Answer: B

Explanation:
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.

NEW QUESTION # 85
A Generative AI Engineer is implementing a supervisor agent and two specialist agents in Databricks: a Sales Analyst for revenue questions and an HR Analyst for staff questions. Each specialist must retrieve data only from its own governed domain, and the engineer wants to preserve that separation using Databricks-native data access for each agent rather than building custom retrieval logic.
What should the engineer implement?

A. Create two separate Genie Spaces for Sales and HR, each scoped to its own governed datasets, and have each specialist agent call the appropriate Space through the API.
B. Create separate Knowledge Assistants for Sales and HR and have each specialist retrieve from the corresponding assistant.
C. Create a shared Genie Space over both domains, but use distinct service principals and Unity Catalog grants for each specialist agent's API access.
D. Create a single Genie Space over both domains and rely on the supervisor agent to route only sales questions to the Sales Analyst and HR questions to the HR Analyst.

Answer: A

Explanation:
Separate Genie Spaces provide domain-specific access to structured business datasets through Databricks- native conversational querying. The Sales specialist calls the Sales Space, and the HR specialist calls the HR Space. Each Space should contain the appropriate datasets and business context, with Unity Catalog permissions enforcing access to its underlying resources. Separating Spaces makes domain boundaries explicit and avoids relying only on an LLM's routing instructions. Knowledge Assistants primarily support document-based knowledge retrieval, making Genie the better fit for structured business questions. A shared Space with distinct permissions can enforce security, but separate Spaces better express the requested domain separation. The video's phrase "HR Analyst for stock questions" appears inconsistent; "staff questions" is an inferred typing correction. Databricks documentation

NEW QUESTION # 86
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?

A. MLflow Model Registry webhooks and a downstream audit log stored in an external database.
B. MLflow experiment tags and an automatically scripted changelog stored in a Databricks notebook.
C. MLflow autologging and Delta Lake time travel on the inference table.
D. MLflow Prompt Registry version history and logged runs that reference the prompt name and version used during inference.

Answer: D

Explanation:
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

NEW QUESTION # 87
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