Exam Name: IBM watsonx Generative AI Engineer - Associate
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Corresponding Certification: IBM Certified watsonx Generative AI Engineer - Associate
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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment & Enterprise Readiness | - Improving solutions based on user feedback - Managing usage and monitoring at a basic level - Preparing GenAI solutions for enterprise usage - Understanding basic security and access control requirements | |
| Topic 2: Prompt Engineering & Output Quality | 25% | - Controlling response style, length, and format - Improving output quality using prompt design techniques - Writing effective and professional prompts - Reducing hallucinations and improving overall output accuracy - Understanding foundational Prompt Engineering techniques |
| Topic 3: Retrieval-Augmented Generation (RAG) | 17% | - Describe when to use a vector database - Generate vector embeddings utilizing models - Describe embeddings in the context of GenAI - Develop using libraries |
| Topic 4: Integration with Model Orchestration | 8% | - Understand real-world Integration Scenarios - Integrate watsonx.ai with Other Services/Manage APIs and SDKs - Develop LLM based applications with LangChain - Orchestrate AI Workflows |
| Topic 5: Analyze and Design a Generative AI Solution | 15% | - Understand how to choose the appropriate model for a use case - Articulate the components in Gen AI Patterns - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Understand use cases and identify Gen AI application opportunities - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Understand the five capabilities of GenAI/LLMs - Understand the limitations of GenAI/LLMs - Articulate the optimal model architecture based on a use case |
| Topic 6: Deployment | 13% | - Plan for a deployment based on client needs - Deploy AI Assets - Plan out deployment of prompts for versioning - Deploy a custom model - High level architecture for deployment options |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back-end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer's intent.
Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?
- A. Utilize IBM watsonx's API orchestration layer to dynamically route requests to the correct system based on intent analysis.
- B. Use synchronous REST APIs for all back-end systems, relying on watsonx's built-in intent recognition to manage API routing.
- C. Implement a monolithic architecture where all system interactions are handled within a single service.
- D. Set up IBM watsonx to queue customer interactions and process them sequentially using a single system at a time.
Correct Answer: A 🗳️
You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?
- A. The model might produce content that doesn't align with the cultural preferences of the user.
- B. The model can generate overly creative or non-factual responses, leading to brand reputation damage.
- C. The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
- D. The model can generate outputs that are too general, failing to meet the specific needs of the user.
Correct Answer: C 🗳️
You have been assigned the task of fine-tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format.
Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?
- A. Defining task-specific instructions and fine-tuning them through prompt design.
- B. Pre-processing and augmenting the training data to improve the model's generalization capabilities.
- C. Running evaluation metrics on the baseline model to measure initial performance.
- D. Deploying the model in a real-time environment for user feedback collection.
Correct Answer: A 🗳️
You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?
- A. "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]." - B. "Example 1: [First Contract]
- C. "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]." - D. "Generate a summary for [New Contract]."
Correct Answer: A 🗳️
You have successfully deployed a custom model using IBM Watson Machine Learning. Now, an application needs to access the model for inference.
Which of the following configurations will ensure that the application has appropriate access to the deployed model?
- A. Use the default IAM policies for all users in the IBM Cloud project
- B. Assign the "viewer" role to the application's service account in Watson Machine Learning
- C. Implement OAuth2 authentication using IBM Cloud IAM for securing the API.
- D. Create a public-facing API endpoint without authentication
Correct Answer: C 🗳️
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