Free Practice Questions for IBM watsonx Generative AI Engineer v1 - Associate (C1000-185) Certification

1.1. Understand the 5 Capabilities of GenAI models/LLMs - Define and describe the five key capabilities of generative AI and large language models (LLMs) - Summarization - Classification - Generation - Code - Translation - Extraction - Q&A - Review case studies or examples demonstrating each capability. - Evaluate the impact of these capabilities in various industries - Discuss potential future developments in LLM capabilities.

1.2. Articulate the Components in Gen AI Patterns - Identify common patterns used in generative AI solutions. - Mixture of experts (MoE) - Variation Auto Encoders (VaE) - Transformer based models - Reasoning models - Describe the components that constitute these patterns (e.g., input data processing, model training, output generation). - Analyze real-world examples to illustrate how these patterns are applied. - Create a diagrammatic representation of various generative AI patterns.

1.3. Understand the Limitations of GenAI/LLMs - List the technical and ethical limitations of generative AI and LLMs. - Discuss scenarios where generative AI may produce biased or incorrect outputs. - Explore strategies to mitigate these limitations in practical applications. - Evaluate the risks associated with the deployment of generative AI in sensitive or critical applications.

1.4. Understand Use Case and Identify Gen AI Application Opportunities - Study various industry sectors to identify potential use cases for generative AI. - Conduct needs analysis to determine how generative AI can address specific business problems. - Propose generative AI solutions for identified use cases. - Prepare feasibility reports for the proposed solutions.

1.5. Understand How to Choose the Appropriate Model for a Use Case - Analyze the requirements of a given use case. - Determine criteria for selecting an appropriate model based on performance, efficiency, cost, and ethical considerations. - Model selection based on Parameter size - Chat vs instruct - IBM Granite models - Billing classes - Simulate decision-making processes to select the optimal model for a use case.

1.6. Articulate the Optimal Model Architecture Based on Use Case - Define model architecture and its significance in AI solutions. - Examine various model architectures and their components. - Match model architectures with specific use cases based on technical requirements. - Agentic architectures

1.7. Identify and apply various tools and techniques like AI agents, RAG , LangChain , etc. - Understand the RAG Pattern - Introduction to the RAG pattern to Basic Search - Demonstrate the RAG pattern with LangChain - AI agents

1.8. Understand security risks associated with LLMs, prompt engineering, prompt, and data - Understand the Security Risks Associated with Inputs - Data Bias - Data Poisoning - Data Curation and Downstream Retraining - Data Privacy - Prompt Injection - Prompt Leaking - Understand the Security Risks Associated with Output - Output and Decision Bias - Revealing Confidential or Personal Information - Toxic Output - Spreading Miss disinformation and toxicity - Harmful code generation - Understand the Security and Privacy for Foundation Models - Guardian models

2.1. Differentiate between zero-shot and few-shot prompting - Introduction to Zero-shot prompting - Introduction to few-shot prompting - Try Zero-shot prompt sample - Try Few-shot prompt sample

2.2. Design Prompts based on use case - Choose right model for a use case. - Try to converse with model for a chat use case. - Try to converse with model to translate from one language to another.

2.3. Generate Prompt Templates - Introduction to Prompt templates - Evaluate Prompt Templates - Creating Environment Templates - Deploying Prompt Templates - Tracking Prompt Templates

2.4. Determine the best model parameters for each GenAI prompt - Introduction to model parameters - Decoding - Greedy Decoding - Sampling Decoding - Understanding the Random Seed - Understanding the Repetition Penalty - Understanding the Stopping Criteria - Understanding the Stop Sequences - Understanding the Minimum and Maximum Tokens

2.5. Describe the benefits of using prompt variables - Articulate what a prompt variable is - Identify optimal static prompt text to replace with variable - Summarize the value of reusable prompts

2.6. Describe the benefits of Prompt Lab - Articulate the prompt editing options - Chat - Structured - Freeform - Demonstrate how to build reusable prompts - Describe example input prompts and their use

2.7. Controlling model parameters - Articulate the decoding process at a high level - Greedy decoding - Sampling decoding - Describe the sampling decoding parameters - Temperature - Top K - Top P - Random Seed - Repetition penalty - Articulate stopping criteria and examples - Stop sequences - Minimum and Maximum tokens - model generation time limit

2.8. Articulate model risks - Describe hallucinations in model output - Underlying causes - Techniques for avoiding - Articulate the risks associated with personal information - Identify Personal information - Techniques for excluding - PII filter - Hate speech, abuse, and profanity - Techniques for reducing risk - HAP filter - Bias - Underlying cause of bias in outputs - Techniques for reducing bias - Debate the risks of data bias and poisoning

3.1. Understand the difference between hard and soft prompts - Differentiate between hard prompts and soft prompts - Designed by humans or AI - Readability of the prompt - Explainability - Articulate how soft prompts are generated - Debate the benefits and drawbacks of soft prompts - Performance - Simplicity - Interpretability - Explainability

3.2. Reconstruct prompts to reduce the cost of using GenAI models - Manage the token usage of each prompt template and model - Detect inefficient prompt techniques - Design cost-effective prompt templates - Employ model parameters to reduce the generation cost - Stop sequences - Min/max token limits

3.3. Plan for Data elements for application usage - Add data to a watsonx.ai project for tuning - Inspect and validate data elements using Data Refinery

3.4. Articulate model quantization techniques - Understand quantization is in the context of LLMs - Tradeoffs - Reduce precision - Reduce computational costs - Techniques

3.5. LoRA

3.6. Prepare the dataset for training - Summarize the purpose of taxonomy tree-based curation - Generate synthetic data using InstructLab - Describe LAB (Large-scale Alignment for chatBots) methodology

3.7. Customize LLMs with InstructLab - Describe what the components of InstructLab - Taxonomy driven data curation - Large scale synthetic data generation - Iterative, large scale alignment tuning - Knowledge tuning - Skill tuning - Articulate the InstructLab workflow

3.8. Generate synthetic data using the User Interface - Describe the two options supported - Leverage your existing data - Create from your custom data schema - Understand limitations on importing existing data sources and size limitations. - Understand anonymization of imported data - Understand the two algorithms for mimicking existing data - Kolmogorov-Smirnov - Anderson-Darling - Understand differential privacy concepts and settings - Privacy budget - Privacy leakage probability - Random seed - Understand sizing requirements for synthetic data generator

4.1. Describe what embeddings are in Context of GenAI - Understand concepts of text embeddings. - Describe different embedding models - IBM Embedding models - Third party embedding models

4.2. Generate vector embeddings utilizing models - Perform converting text to embedding vectors - Describe prerequisites for embedding API - Credentials - Project ID - Deployment ID - Understand the choices and capabilities of vector databases - Purpose built vector databases - Vector extensions to popular databases

4.3. Describe when to use a vector database - Understand the concept of a retriever - Describe different types of retrievers - Vector databases - Embedded - Static - Watson Discovery - GitHub code retrieval API - watsonx Discovery - Understand the capabilities of retrievers - Understand use case driven selection of a retriever.

4.4. Develop using libraries and tools - Understand the RAG Pattern - Understand implementation details of a RAG pattern - LangChain and WatsonX LLM - LangChain, Watson ML and ElasticSearch - LangChain, SingleStore - LlamaIndex - Understand chunking/text splitting - Agentic RAG - AutoRAG

5.1. Plan a deployment based on client needs - Define the deployment lifecycle of a prompt template - Manage changes to prompts templates in applications - Understand roles involved with deploying AI Assets - Understand need around AI Governance - Model performance - Evaluate inferences - Explain outcomes from prompts

5.2. Deploy AI Assets - Benefits of deployments space - Govern lifecycle (dev/evaluation/production) - Separate endpoints for applications to call - Benefits when deploying a prompt template - Changes to application when using a deployment space

5.3. Deploy a custom model - Understand requirements of watsonx and foundation model - Application access to a custom model

5.4. High-level architecture for deployment options - Version using deployment spaces - Changes to applications on a prompt version change - Testing new prompt template version - Model gateway

5.5. Plan the deployment of prompts for versioning - Understand the options between managed Software as a Service or Software - Deployment choices based Architectural pattern - RAG - Summarization - Q&A - Corpus of Data repository - AI Pipelines to update Repository - Manage data in corpus - Adjusting text chunk size - Identify endpoint security and stability - Code generation within PromptLab

6.1. Integrate watsonx.ai with Other Services - Connect watsonx.ai to other IBM Cloud services, such as Watson Assistant for chatbot development or Watson Discovery for document understanding, to build end-to-end AI solutions. - Identify relevant IBM Cloud services that can be integrated with watsonx.ai (e.g., Watson Assistant, Watson Discovery). - Understand the integration points and data exchange mechanisms between watsonx.ai and the chosen services. - Configure the integration using watsonx.ai's UI or APIs. - Integrate with watsonx.governanance - Leverage watsonx.ai's APIs and SDKs to integrate generative AI models with external - applications, platforms, or custom workflows, including utilizing LangChain for complex chain creation and management. - Obtain API keys or credentials for external applications or platforms. - Write code using watsonx.ai's SDKs to send requests and receive responses from the AI models. - Handle errors and exceptions during API interactions.

6.2. Orchestrate AI Workflows - Design and implement workflows utilizing watsonx.ai that combine multiple tasks, including LangChain-based chains, to orchestrate complex generative AI processes. - Break down a complex AI task into smaller, manageable steps. - Design a workflow using orchestration tools. - Utilize orchestration tools to automate the execution of workflows, including scheduling, dependency management, and error handling. - Schedule workflows to run automatically at specific times or intervals. - Implement error handling and retry mechanisms to ensure robustness. - Use conditional logic to trigger different workflow branches based on data or model results. - Monitor and troubleshoot workflow execution, identifying bottlenecks, failures, or performance issues. - View workflow logs and execution details within watsonx.ai. - Identify and resolve errors or bottlenecks that may occur during workflow execution. - Optimize workflow performance by adjusting parameters or parallelizing tasks. - Agentic RAG

6.3. Understand real-world Integration Scenarios - Given a business use case, design a comprehensive solution that integrates watsonx.ai with relevant data sources, other IBM Cloud services, and external systems. - Identify the necessary data sources, IBM Cloud services, and external systems to be integrated. - Design a high-level architecture diagram outlining the data flow and interactions between components. - Implement the designed solution, leveraging the appropriate watsonx components and tools. - Implement the integration using the chosen tools and technologies. - Write code to connect data sources, invoke watsonx.ai APIs, and interact with external systems. - Test the integration in a development environment. - Test and validate the integration, ensuring seamless data flow, reliable communication, and expected functionality. - Deploy the integrated solution to a production or staging environment. - Perform end-to-end testing to verify functionality and data flow. - Monitor the solution's performance and address any issues that arise. - Model Context Protocol

6.4. Develop LLM based applications with LangChain - Explain the core concepts of LangChain (chains, agents, tools, memory) and their role in building complex conversational AI and generative AI applications. - Define the core concepts of LangChain, such as chains, agents, tools, and memory. - Explain how LangChain simplifies the development of complex conversational AI and generative AI applications. - Describe the benefits of using LangChain, such as modularity, reusability, and extensibility. - Demonstrate the ability to create and customize LangChain chains, incorporating various components like LLMs, prompt templates, and external data sources. - Create a simple LangChain that combines an LLM with a prompt template. - Customize a LangChain by adding custom components, such as a tool for interacting with an external API. - Design and implement LangChain agents that can interact with the environment, make decisions, and take actions based on model outputs and external information. - Design a LangChain agent that can perform a specific task, such as answering questions or generating summaries.