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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are fine-tuning a generative AI model using Tuning Studio for a legal document analysis task. The model needs to perform well in summarizing long, complex legal texts while minimizing the amount of computational resources used. You are tasked with optimizing the tuning process to ensure maximum efficiency and model accuracy.
Which of the following actions would most effectively optimize the tuning process in Tuning Studio for this task?

A) Increasing the batch size to reduce the number of gradient updates during training, thus speeding up the process.
B) Adjusting the learning rate dynamically during training based on the model's real-time loss values.
C) Using a fixed number of training epochs to ensure consistent results across tuning iterations.
D) Reducing the number of layers in the model to decrease computational overhead.

2. You are preparing a dataset for fine-tuning a model to classify customer complaints by category. The dataset is imbalanced, with 70% of the data representing complaints about billing, 20% representing complaints about technical issues, and 10% representing complaints about product quality.
Which of the following actions would help address the imbalance while preparing the dataset for fine-tuning? (Select two)

A) Leave the dataset as-is, trusting the model to handle the imbalance using its internal mechanisms.
B) Apply class weighting during model training instead of modifying the dataset itself.
C) Undersample the majority class (billing complaints) to balance the dataset.
D) Oversample the under-represented classes to ensure a balanced distribution across categories.
E) Add more synthetic data for the minority classes by using generative techniques like GPT-3 to create realistic complaints.

3. In the context of generative AI, you are tasked with optimizing a model's performance for a variety of use cases by tuning the prompts. One of your colleagues mentions using a "soft prompt" to improve the model's adaptability.
What best describes the difference between a hard prompt and a soft prompt?

A) Soft prompts are more readable and natural, whereas hard prompts consist of short, technical instructions.
B) A soft prompt is a fixed string of text used in fine-tuning, while a hard prompt adjusts dynamically based on input data.
C) Hard prompts are less efficient because they need to be re-trained with each task, while soft prompts are more versatile and adaptive across multiple tasks.
D) A hard prompt explicitly specifies all constraints, while a soft prompt relies on implicit learning from continuous inputs during training.

4. In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?

A) Randomly generating sentences without adhering to the task-specific instructions
B) Generating synthetic data that overrepresents the simplest task cases to reduce computational load
C) Using domain-specific templates to generate synthetic data that reflects the target use case
D) Avoiding any post-processing or filtering of synthetic data to retain diversity

5. You are developing a generative AI application using LangChain, and you want the system to perform actions like searching a database or retrieving live web content based on a user's request.
How can you best incorporate tools in LangChain to enable the AI to perform such tasks autonomously?

A) Rely on LangChain's memory module to remember previous user queries and provide real-time data access.
B) Use a LangChain agent with a predefined set of tools to dynamically select and invoke the appropriate tool (e.g., database access, API call) based on the user's request.
C) Build a LangChain chain that uses user inputs to sequentially call all the available tools and pick the one with the most relevant output.
D) Configure LangChain to automatically load data from static sources based on historical query patterns, avoiding the need for dynamic tool selection.

Solutions: