IBM watsonx Generative AI Engineer - Associate : C1000-185

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question #1
When optimizing the tuning process in IBM Watsonx Tuning Studio for a Generative AI model, which approach would best reduce training time and computational cost while maintaining model performance?

A. Focus the tuning on adjusting only the model's last few layers, which are responsible for task-specific outputs, while leaving the majority of the model unchanged.
B. Perform full-scale retraining of the model for each new task to ensure maximum adaptability and accuracy.
C. Increase the batch size and reduce the learning rate simultaneously to speed up the tuning process and minimize training iterations.
D. Use all available training data, including unrelated examples, to ensure the model has a broad understanding of multiple tasks before tuning.

Question #2
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 automatically generates prompt templates that can be used for different tasks without further configuration.
B. Tuning Studio allows for the customization of training data at runtime without needing pre-processing.
C. Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.
D. Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.

Question #3
A client is planning to deploy a Watsonx Generative AI model and has raised concerns about ethical usage, bias, and accountability in decision-making.
Which of the following is the most critical step to ensure AI governance during the deployment phase of the model?

A. Implementing a feedback loop for continuous model improvement
B. Monitoring and auditing AI decisions for bias and fairness
C. Testing the model's accuracy on a large set of random data
D. Training the model on additional data to improve accuracy

Question #4
You are tasked with designing a prompt for a sentiment analysis model based on a large language model (LLM). The goal is to generate a coherent response from the model that aligns with a particular sentiment (positive, negative, or neutral) for customer reviews of a product.
Which of the following prompt designs are best suited to generate a positive review response? (Select two)

A. "Write a neutral review, neither praising nor criticizing the product."
B. "Describe the product as if you were a very satisfied customer, and you were recommending it to a friend."
C. "Generate a positive review about the product, focusing on the key strengths and avoiding any negative aspects."
D. "Write a review about the product that highlights both its pros and cons."
E. "Analyze the product based on the customer feedback and write a review that covers all sentiments."

Question #5
You are tasked with generating high-quality responses from a large language model for a customer support application. You want to minimize the amount of provided examples while ensuring that the model generates relevant and specific answers.
Which of the following statements best differentiates between zero-shot and few-shot prompting in this context? (Select two)

A. Zero-shot prompting does not require any examples in the input prompt, while few-shot prompting uses a limited number of examples to guide the model's response.
B. Few-shot prompting improves model performance for unfamiliar tasks by fine-tuning weights based on examples, while zero-shot prompting leaves the model weights unchanged.
C. Few-shot prompting involves fine-tuning the model on a specific dataset before generating output, whereas zero-shot prompting uses pre-trained knowledge without additional fine-tuning.
D. Zero-shot prompting is better suited for tasks requiring domain-specific knowledge, while few-shot prompting is better for general knowledge tasks.
E. ct selection
F. In zero-shot prompting, the model's response is generated purely based on pre-trained knowledge and the structure of the task, while in few-shot prompting, the examples provided offer the model additional context.

Solutions: