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100% Pass Quiz 2025 IBM Perfect C1000-185: Exam IBM watsonx Generative AI Engineer - Associate Syllabus

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q308-Q313):

NEW QUESTION # 308
You are deploying an AI model to a production environment using watsonx.ai. The model is a fine-tuned GPT-based generative model designed to support real-time customer interactions. The deployment must ensure scalability, security, and maintain high availability.
Which deployment strategy should you choose to best meet these requirements?

A. Deploy the model on an on-premises server with an isolated environment to ensure full control and security.
B. Use a containerized deployment with Kubernetes on a multi-node GPU cluster to support auto-scaling and fault tolerance.
C. Deploy the model on a single cloud-based GPU instance, and enable scheduled maintenance to minimize downtime.
D. Deploy the model on multiple virtual machines, manually balancing traffic between instances.

Answer: B

NEW QUESTION # 309
When using Tuning Studio in IBM watsonx, which of the following types of models is most suitable for fine-tuning in a domain-specific task to optimize performance?

A. A model trained from scratch on a small, specific dataset.
B. A pre-trained model with very few layers to ensure fast processing speeds.
C. An untrained model with a minimal number of layers to reduce complexity.
D. A pre-trained model that has been trained on a large, general-purpose dataset.

Answer: D

NEW QUESTION # 310
In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

A. It reduces the time required to train models by lowering the accuracy threshold.
B. It provides real-time feedback on model deployment success rates.
C. It limits the number of model versions stored, improving system performance.
D. It allows for the precise allocation of computational resources to manage budget constraints.

Answer: D

NEW QUESTION # 311
In a scenario where a developer is creating reusable prompt templates for a Watsonx Generative AI project, what is the most effective method to track the usage and performance of these templates over time?

A. Leveraging Watsonx's native analytics and monitoring tools with built-in prompt tracking features
B. Embedding unique identifiers in the prompt templates and using Watsonx's logging mechanisms
C. Using static prompt templates without any tracking, as tracking adds unnecessary overhead
D. Relying on manual tracking of templates in a spreadsheet for each generation

Answer: B

NEW QUESTION # 312
In IBM Watsonx Generative AI, top-p sampling (nucleus sampling) is a parameter that influences the decoding process.
Which of the following best describes how the top-p parameter works during response generation?

A. Top-p sampling ensures that only the top 1% most probable tokens are chosen, regardless of context.
B. Top-p sampling prioritizes tokens based on their frequency in the training dataset, ignoring their conditional probabilities.
C. Top-p sampling selects the next token only from a fixed number of top tokens, ensuring the most predictable outcome.
D. Top-p sampling adjusts the probability threshold dynamically, allowing the model to sample tokens until the cumulative probability reaches p.

Answer: D

NEW QUESTION # 313
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