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

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.

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.

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.

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.

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.

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

Understand the RAG Pattern

- Introduction to the RAG pattern to Basic Search - Demonstrate the RAG pattern with LangChain

AI agents

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

Introduction to Zero-shot prompting

Introduction to few-shot prompting

Try Zero-shot prompt sample

Try Few-shot prompt sample

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.

Introduction to Prompt templates

Evaluate Prompt Templates

Creating Environment Templates

Deploying Prompt Templates

Tracking Prompt Templates

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

Articulate what a prompt variable is

Identify optimal static prompt text to replace with variable

Summarize the value of reusable prompts

Articulate the prompt editing options

- Chat - Structured - Freeform

Demonstrate how to build reusable prompts

Describe example input prompts and their use

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

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

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

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

Add data to a watsonx.ai project for tuning

Inspect and validate data elements using Data Refinery

Understand quantization is in the context of LLMs

- Tradeoffs - Reduce precision - Reduce computational costs - Techniques

LoRA

Summarize the purpose of taxonomy tree-based curation

Generate synthetic data using InstructLab

- Describe LAB (Large-scale Alignment for chatBots) methodology

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

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

Understand concepts of text embeddings.

Describe different embedding models

- IBM Embedding models - Third party embedding 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

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.

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

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

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

Understand requirements of watsonx and foundation model

Application access to a custom model

Version using deployment spaces

Changes to applications on a prompt version change

Testing new prompt template version

Model gateway

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

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.

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

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

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.