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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment & Enterprise Readiness | - Preparing GenAI solutions for enterprise usage - Improving solutions based on user feedback - Understanding basic security and access control requirements - Managing usage and monitoring at a basic level | |
| Topic 2: Deployment | 13% | - Plan for a deployment based on client needs - Deploy a custom model - Plan out deployment of prompts for versioning - Deploy AI Assets - High level architecture for deployment options |
| Topic 3: Prompt Engineering & Output Quality | 25% | - Understanding foundational Prompt Engineering techniques - Writing effective and professional prompts - Improving output quality using prompt design techniques - Reducing hallucinations and improving overall output accuracy - Controlling response style, length, and format |
| Topic 4: Retrieval-Augmented Generation (RAG) | 17% | - Generate vector embeddings utilizing models - Develop using libraries - Describe embeddings in the context of GenAI - Describe when to use a vector database |
| Topic 5: Integration with Model Orchestration | 8% | - Develop LLM based applications with LangChain - Orchestrate AI Workflows - Understand real-world Integration Scenarios - Integrate watsonx.ai with Other Services/Manage APIs and SDKs |
| Topic 6: Analyze and Design a Generative AI Solution | 15% | - Articulate the components in Gen AI Patterns - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Understand how to choose the appropriate model for a use case - Understand use cases and identify Gen AI application opportunities - Articulate the optimal model architecture based on a use case - Understand the five capabilities of GenAI/LLMs - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Understand the limitations of GenAI/LLMs |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?
- A. Use a higher learning rate to improve model convergence
- B. Analyze token distribution patterns in the generated outputs
- C. Increase the number of training epochs to further refine the model
- D. Apply model quantization to reduce the complexity of the output
Correct Answer: B 🗳️
When selecting parameters to optimize a prompt-tuned model experiment in IBM watsonx, which parameter is the most critical for controlling the model's ability to generate coherent and contextually accurate responses?
- A. Gradient clipping
- B. Batch size
- C. Dropout rate
- D. Learning rate
Correct Answer: D 🗳️
Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.
Which of the following roles is responsible for ensuring that the model is properly integrated into the client's existing systems and that data pipelines are established for continuous model improvement?
- A. Solution Architect
- B. Data Engineer
- C. DevOps Engineer
- D. Data Scientist
Correct Answer: A 🗳️
You are building a question-answering system using a Retrieval-Augmented Generation (RAG) architecture. You are deciding whether to incorporate a vector database into the system to handle the document embeddings.
Under which of the following circumstances is the use of a vector database most appropriate?
- A. When the text corpus consists entirely of predefined categories that can be handled by simple keyword matching algorithms
- B. When real-time similarity search over high-dimensional embeddings is needed for large-scale unstructured text data
- C. When the data consists primarily of binary files such as images and videos, and full-text search is required
- D. When the corpus consists mainly of short, structured text like JSON records and traditional SQL indexing will suffice
Correct Answer: B 🗳️
You are preparing a dataset for fine-tuning a model to classify customer complaints by category. The dataset is imbalanced, with 70% of the data representing complaints about billing, 20% representing complaints about technical issues, and 10% representing complaints about product quality.
Which of the following actions would help address the imbalance while preparing the dataset for fine-tuning? (Select two)
- A. Oversample the under-represented classes to ensure a balanced distribution across categories.
- B. Apply class weighting during model training instead of modifying the dataset itself.
- C. Undersample the majority class (billing complaints) to balance the dataset.
- D. Leave the dataset as-is, trusting the model to handle the imbalance using its internal mechanisms.
- E. Add more synthetic data for the minority classes by using generative techniques like GPT-3 to create realistic complaints.
Correct Answer: A,B 🗳️
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