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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Operationalization | 13% | - Monitoring and performance optimization - Versioning and lifecycle management - Deployment planning and architecture - Model and prompt deployment |
| Topic 2: Integration and Orchestration | 8% | - Integration with external services - API and SDK usage - Workflow orchestration with LangChain |
| Topic 3: Prompt Engineering | 16% | - Prompt Lab usage and best practices - Prompting techniques: zero-shot, few-shot, chain-of-thought - Prompt optimization and cost reduction - Prompt design and template creation - Model parameters and hyperparameter tuning |
| Topic 4: Analyze and Design a Generative AI Solution | 15% | - Model architecture and selection criteria - Evaluation metrics and success criteria - Use case analysis and requirements definition - Generative AI and LLM capabilities |
| Topic 5: Model Customization and Fine-Tuning | 31% | - Data preparation and dataset creation - Customization with InstructLab - Fine-tuning concepts and approaches - Synthetic data generation - Model quantization and optimization - Parameter-Efficient Fine-Tuning (PEFT), LoRA |
| Topic 6: Retrieval-Augmented Generation (RAG) | 17% | - Embedding models and vector representations - Vector databases and similarity search - RAG architecture and implementation - Integration with watsonx.data |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
1. After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)
A) Small difference between training and validation loss
B) Low validation loss
C) High training accuracy
D) Low training loss
E) Large difference between training and validation accuracy
2. Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?
A) Post-training dynamic quantization
B) Quantization-aware training (QAT)
C) Post-training static quantization
D) Hybrid quantization
3. You are tasked with deploying a custom Watsonx Generative AI model for a client who requires low-latency responses and scalability to handle unpredictable traffic.
Which deployment architecture would best meet the client's requirements?
A) Using a serverless architecture with auto-scaling
B) Deploying the model in a local on-premise data center
C) Deploying the model on a single dedicated server
D) Using a containerized architecture without orchestration
4. Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?
A) Temperature Sampling with temperature = 0.0
B) Nucleus Sampling (Top-p) with p = 0.9
C) Greedy Decoding
D) Beam Search Decoding with a small beam size (e.g., 2)
5. In the context of Generative AI, which of the following is an appropriate method for setting a stopping criterion to prevent a model from generating excessively long sequences?
A) Allowing the model to continue until it reaches the minimum token length specified
B) Stopping the generation when the model reaches the end of its training dataset
C) Setting a maximum token length for the output sequence
D) Stopping the model based on the number of unique tokens generated
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: C |
