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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q232-Q237):

NEW QUESTION # 232
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 eliminates the need for any human intervention in the fine-tuning process.
C. It improves the model's generalization by exposing it to a wider variety of data points and scenarios.
D. It automatically anonymizes sensitive data points to comply with data privacy regulations during the synthetic data generation process.

Answer: C

NEW QUESTION # 233
Your team has developed an AI model that generates automated legal documents based on user inputs. The client, a large law firm, wants to deploy this model but has stringent security, compliance, and auditability requirements due to the sensitive nature of the data.
What is the most appropriate deployment strategy to meet these specific requirements?

A. Deploy the model using a serverless architecture to minimize operational overhead and maintain compliance.
B. Deploy the model on a public cloud with built-in encryption and use APIs to connect to the client's private data.
C. Deploy the model on a hybrid cloud, with inference done on the client's on-premise servers and training done in the public cloud.
D. Use a private cloud with role-based access controls (RBAC) and ensure model activity is logged for auditing purposes.

Answer: D

NEW QUESTION # 234
When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

A. It allows incremental training, saving computational resources by reusing checkpoints.
B. It expands the model's architecture to handle larger datasets.
C. It automatically increases the number of layers for more complex tasks.
D. It optimizes the model for multilingual capabilities by adding new embeddings.

Answer: A

NEW QUESTION # 235
You are tasked with generating synthetic data for a fine-tuning task on an IBM watsonx model. The goal is to mimic the distribution of existing training data while ensuring the synthetic data maintains its statistical similarity to the original. You are provided with two algorithms, Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, to assess the similarity between the original and synthetic data distributions.
Which of the following best describes how you should implement synthetic data generation using the User Interface and choose the correct algorithm?

A. Use Algorithm A (Kolmogorov-Smirnov Test) to compare the original and synthetic data distributions, checking for deviations across the entire data range.
B. Use the User Interface to generate synthetic data and validate it using Algorithm B, which assesses the overall shape of the distributions but does not provide a significance test for statistical similarity.
C. Use the User Interface to generate synthetic data and validate it using Algorithm A, which compares the distributions' mean values to ensure close alignment.
D. Use Algorithm A (Kolmogorov-Smirnov Test) to match the covariance matrix of the original and synthetic data distributions, ensuring high correlation between data points.

Answer: A

NEW QUESTION # 236
A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?

A. It provides a hierarchical structure that helps the model understand the relationships between different medical concepts.
B. It eliminates the need for data cleaning and preprocessing since the taxonomy provides the structure.
C. It reduces the overall size of the dataset by filtering out irrelevant data.
D. It ensures that the model only focuses on specific medical specialties by eliminating other categories.

Answer: A

NEW QUESTION # 237
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