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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 pre-trained language model for a customer support chatbot. The dataset you're using is mostly unstructured text from chat logs.
What steps should you take to prepare the dataset for fine-tuning to ensure optimal model performance?

A) Use data augmentation techniques like paraphrasing to artificially increase the dataset size.
B) Randomly split the data into training, validation, and test sets.
C) Normalize the text by removing all punctuation, special characters, and converting text to lowercase.
D) Use domain-specific tokenization to better capture important keywords and phrases relevant to customer support.

2. A financial services company is building a generative AI model to assist with customer support. The company is concerned about potential legal liabilities if the model generates customer information, such as bank account numbers or personal identification data, as part of its responses.
Which of the following techniques would best mitigate the risk of generating Personally Identifiable Information (PII) during inference?

A) Train the model on sensitive customer data but ensure that the temperature is set low to avoid generating diverse outputs.
B) Set a strict token limit to prevent the model from generating long sequences, assuming PII tends to appear in longer outputs.
C) Use greedy decoding to ensure the model generates only the most probable tokens, which are less likely to include PII.
D) Implement a real-time PII filter that detects and removes sensitive data before the output is presented to the user.

3. IBM Watsonx Tuning Studio allows users to fine-tune pre-trained models for their specific use cases.
Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?

A) It fully retrains the base model from scratch, ensuring the highest possible accuracy for each new task, regardless of prior training.
B) It significantly reduces the computational costs associated with model fine-tuning by only updating the model's parameters relevant to the specific task, preserving the general knowledge of the base model.
C) It enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.
D) It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.

4. After completing a prompt-tuning experiment, you notice that the model's accuracy in generating relevant responses is high, but the fluency and grammatical correctness of the outputs seem to be suboptimal.
What statistical metric would most directly indicate this issue, and what action should you take to improve the output?

A) ROUGE score; adjust the token generation limit to ensure longer outputs.
B) BLEU score; improve prompt engineering to ensure that the model focuses on fluency.
C) F1 score; increase the training dataset size to improve overall accuracy.
D) Perplexity score; apply additional language model fine-tuning on grammatical correctness.

5. You are tasked with creating a prompt template for IBM Watsonx to generate customer support responses based on user queries. The response needs to be polite, concise, and address the issue directly.
Which of the following is the most appropriate structure for a reusable prompt template to ensure consistency across multiple queries?

A) "Generate a detailed and formal response to the customer, focusing on providing as much information as possible, even if it's unrelated to the query."
B) "Generate a professional response to the customer's query, avoiding repetition and unnecessary details, while focusing on addressing the issue succinctly."
C) "Please write a polite and professional response to the customer's query, including any relevant context or background information and focusing on the core issue."
D) "Write a short and casual response to the customer, focusing on being friendly and engaging, regardless of the content of the query."

Solutions: