Databricks Generative AI Engineer Associate Practice Exams

About this course

The Databricks Certified Generative AI Engineer Associate is a specialized professional focused on building and deploying generative AI applications and solutions on the Databricks Lakehouse Platform. This role combines the skills of a data engineer, machine learning engineer, and AI application developer, with a specific emphasis on leveraging large language models (LLMs) and other foundation models. The engineer is responsible for the end-to-end lifecycle of generative AI projects, from preparing the underlying data and selecting or fine-tuning models to deploying them as scalable, secure, and cost-effective applications that can generate text, code, images, or other content.

A core competency for this engineer is expertise in using the Databricks platform for data preparation and feature engineering specifically for generative AI. They work with large, often unstructured datasets, using Apache Spark in Databricks to clean, process, and curate high-quality data for pre-training or fine-tuning foundation models. They are skilled in using tools like Databricks Vector Search to create and manage vector embeddings, which are essential for techniques like retrieval-augmented generation (RAG). This involves chunking documents, generating embeddings using models from Databricks Marketplace or external services, and storing them in a vector database for efficient similarity search, enabling the AI model to access and reason over proprietary knowledge.

Model customization and evaluation are key responsibilities. The engineer is proficient in using Databricks' MLflow for managing the machine learning lifecycle, including experiment tracking and model registry. They use frameworks like Hugging Face Transformers and DeepSpeed, integrated within Databricks, to fine-tune pre-trained LLMs on domain-specific data, adapting the model's behavior and knowledge for a particular business use case. A critical part of this process is rigorous model evaluation. The engineer designs and implements evaluation metrics and

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