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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Generative AI Engineer Associate
Exam Number:	Generative AI Engineer Associate
Exam Format:	Multiple Select, Multiple Choice
Certificate Validity Period:	2 years
Exam Duration:	90 minutes
Passing Score:	Scaled Score (approx. 70%)
Related Certifications:	Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate
Real Exam Qty:	45
Available Languages:	English, Portuguese (Brazil), Korean, Japanese
Exam Price:	$200 USD
Sample Questions:	Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions
Exam Way:	Online (Proctored) or Test Center
Pre Condition:	None (Recommended: 6+ months hands-on experience)
Official Syllabus URL:	https://www.databricks.com/learn/certification/genai-engineer-associate

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

Topic	Details
Topic 1	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 2	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 3	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q32-Q37):

NEW QUESTION # 32
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = " Tell me a {adjective} joke "
prompt = PromptTemplate(input_variables=[ " adjective " ], template=prompt_template)
# ... (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

A. (Incorrect structure)
B. (Incorrect structure)
C. (Incorrect structure)
D. prompt_template = " Tell me a {adjective} joke "
prompt = PromptTemplate(input_variables=[ " adjective " ], template=prompt_template) llm = OpenAI() llm_chain = LLMChain(prompt=prompt, llm=llm) llm_chain.generate([{ " adjective " : " funny " }])

Answer: D

NEW QUESTION # 33
A Generative AI Engineer is integrating Mosaic AI Vector Search into a Retrieval-Augmented Generation (RAG) system. The source data, comprising simple text entries, is stored in a Delta table. To simplify the workflow, the engineer plans to use an embedding model served via a Mosaic AI Model Serving endpoint to automatically compute embeddings during data synchronization from the Delta table to the vector search index.
Which method should the engineer use to achieve this integration?

A. Hybrid embedding computing.
B. Delta Sync index with managed embeddings.
C. Delta Sync index with self-managed embeddings.
D. Direct Vector Access index.

Answer: B

Explanation:
A Delta Sync index with managed embeddings supports the workflow described. The engineer identifies the source text column and configures the embedding model endpoint. Databricks computes embeddings as part of synchronization and maintains the corresponding searchable index. This removes the requirement to calculate vectors independently and write them into the source table before indexing. With self-managed embeddings, the application or upstream pipeline must already provide the embedding vectors. A Direct Vector Access index requires explicit updates through an API or SDK and does not automatically synchronize with the source Delta table. "Hybrid embedding computing" is not the relevant index configuration; hybrid search combines retrieval techniques. Managed embeddings and Delta synchronization therefore address both automatic vector generation and source-data integration. Databricks documentation

NEW QUESTION # 34
A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

A. vsc.similarity_search()
B. vsc.create_delta_sync_index()
C. vsc.create_direct_access_index()
D. vsc.get_index()

Answer: B

Explanation:
Context: The Generative AI Engineer is setting up a vector store using Databricks' VectorSearchClient. This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options:
* Option A: vsc.get_index(): This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index(): After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index(): This function would create an index that directly accesses the data without synchronization. While also a valid approach, it's less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search(): This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, henceOption B.

NEW QUESTION # 35
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

A. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
B. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
C. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint
D. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

Answer: D

NEW QUESTION # 36
A Generative AI Engineer is evaluating a customer-support agent in Databricks. The team needs to score each response on a domain-specific policy: the answer must cite an approved refund rule and must not mention unsupported escalation paths. Built-in evaluation metrics do not capture this logic. The team wants the metric to run during agent evaluation in Databricks and return a repeatable, structured score for each trace.
Which approach should the engineer use?

A. Log the traces to MLflow v3.x and review failures in the UI without defining a scorer.
B. Create a custom MLflow scorer that inspects agent outputs against the policy and pass it into the evaluation run.
C. Use only latency and token-count metrics because custom policy checks are not supported in evaluation workflows.
D. Add the policy text to the system prompt and rely on the model's self-reported compliance as the evaluation result.

Answer: B

Explanation:
MLflow supports custom scorers for application-specific evaluation requirements that built-in metrics do not address. The engineer can define a scorer that examines the generated answer and, when necessary, the complete execution trace. It can verify references against an approved refund-rule list and check whether escalation statements violate the policy. The scorer returns structured feedback, such as a Boolean result, numerical score, or Feedback object, and is supplied to mlflow.genai.evaluate(). Deterministic checks are appropriate when repeatability is essential; semantic checks may require a carefully specified judge. Latency and token counts measure operational behavior rather than policy compliance. Reviewing traces manually does not create an automated metric, and the generating model's self-assessment is not an independent compliance evaluation. Databricks documentation

NEW QUESTION # 37
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