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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 language model using a prompt-tuning approach on a dataset consisting of customer service chat logs. The goal is to optimize the model's ability to generate polite and contextually appropriate responses.
Which of the following steps are essential when preparing the dataset for prompt-tuning in this context? (Select two)

A) Remove any conversations that contain excessive user slang or misspellings.
B) Separate the dataset into training, validation, and test subsets.
C) Ensure all examples in the dataset follow the exact same input-output format.
D) Ensure each conversation includes both customer input and agent response as context for the model.
E) Convert all user queries into lowercase to reduce noise in the dataset.

2. You have just finished a prompt tuning experiment for a large language model (LLM) to optimize its output for generating customer support summaries. The tuning results show that while the accuracy of the generated summaries is high (95%), the response time for generating them has significantly increased. The experiment data suggests that increasing the maximum token length during tuning led to better quality summaries but with slower generation.
Which parameter should you adjust to improve the model's response time without sacrificing too much summary quality?

A) Change the decoding strategy from beam search to greedy decoding
B) Increase the batch size
C) Decrease the maximum token length
D) Decrease the number of epochs

3. You are using IBM's Tuning Studio to fine-tune a generative AI model for a custom text classification task. The model was pre-trained on a large corpus but shows suboptimal performance when applied to your domain-specific data. You aim to improve both accuracy and computational efficiency.
Which of the following is a primary benefit of using Tuning Studio to optimize this model?

A) Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.
B) Tuning Studio automatically generates prompt templates that can be used for different tasks without further configuration.
C) Tuning Studio allows for the customization of training data at runtime without needing pre-processing.
D) Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.

4. During the fine-tuning of a large language model (LLM) with InstructLab for a legal document classification task, you notice that the model performs exceptionally well on the training set but poorly on the validation set.
What could be done to address the overfitting issue and improve the model's generalization? (Select two)

A) Remove all regularization and fine-tune the model on the training set until convergence.
B) Increase the size of the model to better capture complex patterns in the training data.
C) Fine-tune the model for more epochs to ensure the model has fully learned from the training data.
D) Use early stopping based on the validation set performance.
E) Introduce dropout regularization during fine-tuning to prevent overfitting.

5. You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?

A) Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
B) Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.
C) Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
D) Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.

Solutions: