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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q77-Q82):

NEW QUESTION # 77
When deploying a generative AI model using IBM watsonx, which of the following actions should be prioritized to ensure high availability and minimize downtime in production environments? (Select two)

A. Disabling failover mechanisms to reduce deployment complexity
B. Configuring auto-scaling policies based on usage patterns and resource demand
C. Setting up redundant storage for the model's dataset but not for the model itself
D. Deploying the model on a single cloud-based server with load balancing enabled
E. Implementing containerization for the model through Kubernetes

Answer: B,E

NEW QUESTION # 78
You are fine-tuning a generative AI model in IBM Watsonx and need to define appropriate stopping criteria to ensure the generated text is relevant and coherent.
Which of the following would be an example of a valid stopping criterion for a text generation task?

A. The model will stop generating text once a predefined number of characters is reached, ensuring that the output does not exceed a set length.
B. The model will stop generating text once the average probability of each word in the output falls below a 50% threshold.
C. The model will stop generating text once it detects a repetitive sequence of words or phrases, automatically cutting off redundancy.
D. The model will stop generating text when it encounters a special end-of-sequence token or punctuation such as a period or question mark.

Answer: D

NEW QUESTION # 79
You are optimizing a generative AI model using prompts. You are tasked with choosing between a hard prompt and a soft prompt for generating a technical report.
Which option best describes a soft prompt in this context?

A. A manually written prompt that is optimized using a grid search of the best keywords and patterns for the task.
B. A prompt that restricts the model's output using hard-coded rules to ensure specific behavior during generation.
C. A prompt that influences the model by embedding learned vectors into the input, modifying the generation behavior indirectly.
D. A prompt that directly instructs the model with domain-specific keywords and structured language to steer the model's output.

Answer: C

NEW QUESTION # 80
You are tasked with improving the performance of a generative AI model that generates personalized marketing emails. The client wants the model to produce more relevant and targeted emails based on user behavior while keeping token usage and computational costs low. You decide to use Tuning Studio to achieve this.
Which of the following is a key benefit of using Tuning Studio in this scenario?

A. It increases the model's maximum token limit, allowing for more extensive outputs without sacrificing performance.
B. It allows the fine-tuning of the model's hyperparameters based on the specific domain, improving relevance and reducing token generation costs.
C. It provides tools for manual annotation of data to improve model accuracy.
D. It automatically generates custom datasets for training without needing labeled data.

Answer: B

NEW QUESTION # 81
You are tasked with optimizing the cost of text generation using a generative AI model by adjusting model parameters. One of the key parameters you consider is the temperature, which controls the randomness of the output. The client has requested outputs that are more predictable and closer to the intended meaning, without unnecessary creativity, in order to reduce unnecessary token usage and ensure the quality of generated responses. Given the following task:
"Generate a brief summary of a technical article that focuses on key innovations without including unrelated or creative content." Which of the following temperature settings would be most appropriate to optimize cost while maintaining focus and minimizing unnecessary token usage?

A. 0.0
B. 1.0
C. 1.2
D. 0.1

Answer: D

NEW QUESTION # 82
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