Databricks Certified Generative AI Engineer Associate training Course

Module-1 : Design Applications : Design a prompt that elicits a specically formatted response

Module-2: Select model tasks to accomplish a given business requirement

Module-3: Select chain components for a desired model input and output

Select chain components for a desired model input and output

Module-4: Translate business use case goals into a description of the desired inputs and outputs for

Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline

Module-5: Dene and order tools that gather knowledge or take actions for multi-stage reasoning

Dene and order tools that gather knowledge or take actions for multi-stage reasoning

Module-6: Data Preparation: Apply a chunking strategy for a given document structure and model constraints

Module-7: Filter extraneous content in source documents that degrades quality of a RAG application

Module-8: Choose the appropriate Python package to extract document content from provided

Module-9: Dene operations and sequence to write given chunked text into Delta Lake tables in Unity

Module-10: Identify needed source documents that provide necessary knowledge and quality for a

Module-11: Identify prompt/response pairs that align with a given model task

Module-12: Use tools and metrics to evaluate retrieval performance

Module-13: Application Development: Create tools needed to extract data for a given data retrieval need

Module-14: Select Langchain/similar tools for use in a Generative AI application.

Module-15: Identify how prompt formats can change model outputs and results

Module-16: Qualitatively assess responses to identify common issues such as quality and safety

Module-17: Select chunking strategy based on model & retrieval evaluation

Module-18: Augment a prompt with additional context from a user's input based on key elds, terms,

Module-19: Create a prompt that adjusts an LLM's response from a baseline to a desired output

Module-20: Implement LLM guardrails to prevent negative outcomes

1 LLM Guardrails Implementation Incorporate post-processing steps

Module-21: Write metaprompts that minimize hallucinations or leaking private data

Module-22: Build agent prompt templates exposing available functions

Module-23: Select the best LLM based on the attributes of the application to be developed

Module-24: Select a embedding model context length based on source documents, expected queries,

Module-25: Select a model for from a model hub or marketplace for a task based on model metadata/model cards

1 Model Metadata Analysis Use model cards

Module-26: Select the best model for a given task based on common metrics generated in experiments

Module-27: Assembling and Deploying Applications : Code a chain using a pyfunc model with pre- and post-processing

Module-28: Control access to resources from model serving endpoints

Module-29: Code a simple chain according to requirements

Module-30: Code a simple chain using langchain

Module-31: Choose the basic elements needed to create a RAG application: model avor, embedding model, retriever, dependencies, input examples, model signature

Module-32: Register the model to Unity Catalog using MLow

Module-33: Sequence the steps needed to deploy an endpoint for a basic RAG application

Module-34: Create and query a Vector Search index

Module-35: Identify how to serve an LLM application that leverages Foundation Model APIs

Module-36: Identify resources needed to serve features for a RAG application

Module-37: Governance: Use masking techniques as guard rails to meet a performance objective

Module-38: Select guardrail techniques to protect against malicious user inputs to a Gen AI application

Guardrails Against Malicious User Inputs- Define validation rules for acceptable input formats and ranges

Module-39: Recommend an alternative for problematic text mitigation in a data source feeding a RAG application

Module-40: Use legal/licensing requirements for data sources to avoid legal risk

Module-41: Evaluation and Monitoring: Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics

Module-42 Select key metrics to monitor for a specic LLM deployment scenario

Module-43 Evaluate model performance in a RAG application using MLow

Module-44 Use inference logging to assess deployed RAG application performance

Module-45 Use Databricks features to control LLM costs for RAG applications

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Databricks Certified Generative AI Engineer Associate training

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