IBM C1000-185 Test Questions Fee - Latest C1000-185 Exam Forum

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q227-Q232):

NEW QUESTION # 227
You are integrating watsonx.ai into an external system to handle text generation for a content creation application. The external system requires real-time processing and needs to interact with watsonx.ai frequently. Given this requirement, which integration method is most appropriate for ensuring reliable and scalable communication between the external system and watsonx.ai?

A. Integrate watsonx.ai through the SDK to directly embed AI capabilities into the external system, eliminating the need for API calls.
B. Leverage asynchronous REST API calls with callbacks to enable the external system to send requests and continue processing while waiting for the response.
C. Implement Webhooks to receive updates from watsonx.ai when new data is generated and push it to the external system.
D. Use a REST API with synchronous requests, where the external system waits for watsonx.ai to respond before proceeding.

Answer: B

NEW QUESTION # 228
A client has deployed a generative AI model to generate technical support responses for various issues in their product line. To improve the accuracy of the responses, they have been using tuned prompts for each type of technical issue (e.g., network issues, hardware failures). However, the client is concerned about occasional misclassifications when the prompts are reused across similar but distinct issues. They want to optimize the prompts to handle edge cases better without sacrificing the response speed.
What approach would be most appropriate for managing and optimizing these tuned prompts?

A. Use a single general prompt, apply greedy decoding, and manually edit outputs for any misclassifications.
B. Use a single tuned prompt for each product category, apply top-p sampling, and rely on post-processing to correct any misclassifications.
C. Leverage prompt chaining, where an initial general prompt narrows down the issue type, followed by a more specific tuned prompt for the final response.
D. Create highly specific prompts for each possible issue, fine-tune the model on each prompt, and prioritize correctness over speed.

Answer: C

NEW QUESTION # 229
Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

A. Use adversarial training techniques to make the model more robust against bias and poisoning.
B. Apply prompt engineering techniques to avoid triggering known biases in the model.
C. Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.
D. Fine-tune the model using only the training data from trusted sources, without expanding the dataset.

Answer: C

NEW QUESTION # 230
You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

A. Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
B. Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
C. Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
D. Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.
E. Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.

Answer: C,D

NEW QUESTION # 231
You are tasked with deploying a custom Watsonx Generative AI model for a client who requires low-latency responses and scalability to handle unpredictable traffic.
Which deployment architecture would best meet the client's requirements?

A. Deploying the model on a single dedicated server
B. Using a containerized architecture without orchestration
C. Using a serverless architecture with auto-scaling
D. Deploying the model in a local on-premise data center

Answer: C

NEW QUESTION # 232
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IBM C1000-185 Test Questions Fee - Latest C1000-185 Exam Forum

Latest C1000-185 Exam Forum, C1000-185 Dumps Free Download

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q227-Q232):

Pass-Sure C1000-185 Test Questions Fee Spend Your Little Time and Energy to Pass C1000-185: IBM watsonx Generative AI Engineer - Associate exam

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