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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with fine-tuning a large language model (LLM) using IBM's InstructLab to improve performance for a specific customer service task. The goal is to enhance the model's ability to answer questions related to account management and customer complaints.
Which of the following actions is NOT a component of the fine-tuning process in InstructLab?

A) Defining specific task instructions that the model will follow during inference
B) Selecting and preprocessing a representative dataset of customer interactions for training
C) Directly adjusting the model's architecture to increase the number of attention heads in the transformer
D) Tuning the learning rate to prevent overfitting during the fine-tuning process

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) Quantization-aware training (QAT)
B) Post-training static quantization
C) Post-training dynamic quantization
D) Hybrid quantization

3. You are working with IBM Watsonx and need to generate synthetic data to improve your model's performance on a custom domain-specific task. After importing a dataset, you want to use the User Interface to generate this synthetic data.
What is the primary benefit of using synthetic data generation in fine-tuning your model?

A) It creates a larger training dataset by duplicating and randomizing the existing data, which enhances model accuracy.
B) It automatically anonymizes sensitive data points to comply with data privacy regulations during the synthetic data generation process.
C) It eliminates the need for any human intervention in the fine-tuning process.
D) It improves the model's generalization by exposing it to a wider variety of data points and scenarios.

4. In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

A) Latent Dirichlet Allocation (LDA)
B) Bag-of-Words (BoW)
C) One-Hot Encoding
D) Word2Vec

5. You are working on optimizing a generative AI model that will handle large-scale text generation tasks. The current model is slow during inference, and you need to improve its performance without increasing operational costs. You decide to use IBM Tuning Studio for optimization.
Which of the following is the most significant benefit of using Tuning Studio in this scenario?

A) It pre-loads commonly used datasets, reducing the need for data handling during the training process.
B) It provides guidance on reducing the number of parameters in the model to improve inference speed.
C) It automatically scales the model up or down depending on the input data size.
D) It optimizes hyperparameters such as learning rate and batch size to reduce computational overhead during inference.

Solutions: