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Section 1
Design a prompt that elicits a specifically formatted response
Preview
Preview: Design Applications
Preview includes- 4 of 4 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Define the task and audience
- Specify an explicit output schema
- State null/omission and formatting rules
- Add representative examples and constraints
Overview
Turn downstream interface requirements into explicit prompt instructions. Treat schema, field presence, ordering, allowed values, and missing-value behavior as a contract, then evaluate the prompt against representative inputs.
Core Concepts
- State the task before optional context.
- Specify JSON/table/bulleted structure precisely instead of asking for 'structured' output.
- Define what to do when information is missing; for example, return null rather than inventing a value.
Exam Tips
- Write the required fields and missing-value rules first.
- Evaluate malformed, multilingual, and incomplete inputs.
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Section 2
Select model tasks to accomplish a given business requirement
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Section 3
Select chain components for a desired model input and output
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Section 4
Translate business use case goals into a description of the desired inputs and outputs for an AI pipeline
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Section 5
Define and order tools that gather knowledge or take actions for multi-stage reasoning
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Section 6
Determine how and when to use Agent Bricks to solve problems
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Section 7
Apply a chunking strategy for a given document structure and model constraints
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Section 10
Define operations and sequence to write chunked text into Delta Lake tables in Unity Catalog
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Section 11
Identify source documents that provide necessary knowledge and quality for a RAG application
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Section 12
Use tools and metrics to evaluate retrieval performance
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Section 13
Design retrieval systems using advanced chunking strategies
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Section 14
Explain the role of re-ranking in the information retrieval process
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Section 15
Select LangChain or similar tools to use for a GenAI application
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Section 16
Qualitatively assess responses to identify common issues such as quality and safety
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Section 17
Select a chunking strategy based on model and retrieval evaluation
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Section 18
Augment a prompt with context from user input based on key fields, terms, and intents
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Section 19
Create a prompt that adjusts an LLM response from a baseline to a desired output
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Section 20
Implement LLM guardrails to prevent negative outcomes
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Section 21
Select the best LLM based on application attributes
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Section 22
Select an embedding model context length based on source documents, queries, and optimization strategy
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Section 23
Select a model from a model hub or marketplace based on model metadata or model cards
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Section 24
Select the best model for a task based on common metrics generated in experiments
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Section 25
Utilize MLflow and Agent Framework for developing agentic systems
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Section 26
Compare the evaluation and monitoring phases of the Gen AI application life cycle
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Section 27
Enable multi-agent systems to leverage Genie Spaces or conversational APIs to retrieve data
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Section 28
Code a chain using a pyfunc model with pre- and post-processing
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Section 29
Control access to resources from model serving endpoints
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Section 30
Code a simple chain according to requirements
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Section 31
Choose RAG elements such as model flavor, embedding model, retriever, dependencies, input examples, and model signature
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Section 32
Register the model to Unity Catalog using MLflow
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Section 33
Create and query a Vector Search index
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Section 34
Identify how to serve an LLM application that leverages Foundation Model APIs
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Section 35
Explain key concepts and components of Mosaic AI Vector Search / AI Search
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Section 36
Identify batch inference workloads and apply ai_query() appropriately
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Section 37
Configure vector search based on embeddings, update frequency, latency, and cost requirements
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Section 38
Configure a persistent datastore for intermediate memory or structured information
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Section 39
Apply CI/CD practices for Vector Search updates, prompt promotion, and agent component testing
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Section 40
Integrate managed, external, and custom MCP servers
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Section 41
Apply prompt version control and manage prompt lifecycle
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Section 42
Develop an appropriate interactive user-facing interface for a GenAI application
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Section 43
Use masking techniques as guardrails to meet a performance or privacy objective
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Section 44
Select guardrail techniques to protect against malicious user inputs
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Section 45
Apply legal and licensing requirements to data sources used by GenAI applications
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Section 46
Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application
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Section 47
Select an LLM choice based on quantitative evaluation metrics
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Section 48
Select key metrics to monitor for a specific LLM deployment scenario
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Section 49
Evaluate agent performance with MLflow scoring and tracing
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Section 50
Use inference logging to assess deployed RAG application performance
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Section 51
Use Databricks features to control LLM costs
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Section 52
Use inference tables and Agent Monitoring to track a live LLM endpoint
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Section 53
Identify evaluation judges that require ground truth
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Section 54
Use AI Gateway, inference tables, usage tables, and rate limiting to track LLMs or agents
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Section 55
Use Databricks custom Scorers for evaluating agents and LLMs
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Section 56
Use subject matter expert feedback to ground iterative evaluation and improvement
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