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Databricks Databricks-Generative-AI-Engineer-Associate Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 2	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.
Thema 3	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Thema 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.

Referenz: https://www.databricks.com/learn/certification/genai-engineer-associate

Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associate Prüfungsfragen mit Lösungen

Frage #1
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?

A. context length 2048: smallest model is 11GB and embedding dimension 2560
B. context length 512: smallest model is 0.13GB and embedding dimension 384
C. context length 32768: smallest model is 14GB and embedding dimension 4096
D. context length 514; smallest model is 0.44GB and embedding dimension 768

Frage #2
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. Setting chunk size to match the model ' s context window to maximize coverage
B. Implementing metadata filtering based on car models and years
C. Adding few-shot examples for response generation
D. Fine-tuning an embedding model on automotive terminology

Frage #3
A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine-tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

A. Implement AI guardrails to allow users to configure and enforce compliance
B. Apply attribute-based access controls (ABAC) to limit unauthorized access
C. Use secure model features with Databricks Feature Store
D. Leverage Databricks access control lists (ACLs) to configure permissions for accessing models

Frage #4
All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?

A. MLflow Deployments SDK
B. OpenAI client
C. Databricks Python SDK
D. REST API via requests library

Frage #5
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. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
B. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
C. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint
D. 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

Fragen und Antworten:

Frage #1
Antwort: B

Frage #2
Antwort: A

Frage #3
Antwort: A

Frage #4
Antwort: D

Frage #5
Antwort: C