C1000-185 試験問題を無料オンラインアクセス

You are tasked with improving the performance of a generative AI model used for customer service automation. The model needs to respond quickly and with high accuracy, particularly for complex queries. You have access to Tuning Studio as part of your optimization toolkit.
Which of the following is a primary benefit of using Tuning Studio to optimize the model in this scenario?

• A.It provides automated fine-tuning of the model's hyperparameters to improve performance on domain-specific tasks.
• B.It enables the creation of new datasets by generating synthetic data based on prompts.
• C.It allows you to manually edit the output tokens to ensure correctness.
• D.It automates the process of cleaning and preprocessing the input data before model training.

You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.
Which of the following represent significant risks that should be mitigated before full deployment? (Select two)

• A.The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.
• B.The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.
• C.The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.
• D.The model is trained on open-source financial data, which results in slower response times during inference.
• E.The model occasionally generates offensive or inappropriate content when responding to user queries.

As an IBM Watsonx Generative AI engineer, you are tasked with creating a chatbot for a public-facing service. One key concern is ensuring that the model does not generate or propagate hate speech, abusive content, or profanity. To mitigate these risks, you must implement appropriate controls.
Which of the following is the best approach to mitigate hate speech, abuse, and profanity from being generated by your AI model?

• A.Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter to dynamically detect and block harmful content at inference time.
• B.Fine-tune the model with a highly curated dataset that contains labeled examples of hate speech, abuse, and profanity for the model to learn to avoid.
• C.Apply a simple word-level blacklist filter to detect and remove harmful content from the model output.
• D.Train the model only on data that excludes all user-generated content to prevent exposure to harmful language.

You are tasked with deploying a versioned prompt for a customer-facing generative AI application. The prompts are iteratively improved based on feedback, and you need to ensure that each version of the prompt is tracked and accessible for rollback in case a newer version produces worse results.
Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?

• A.Leverage a cloud-based deployment pipeline with integrated versioning that automates prompt rollback and audit tracking.
• B.Deploy prompts directly to production without versioning and manually track changes in a spreadsheet.
• C.Use a version control system like Git to track prompt changes and synchronize the latest version with the deployment environment.
• D.Store each version of the prompt as a separate file in a local folder and manually deploy the desired version when needed.

While working on generating a concise response to a user prompt, you notice that the generative AI model in IBM watsonx is producing excessively long outputs. You want to ensure that the response is informative but doesn't exceed a specific length.
Which of the following parameters should you adjust, and what is the most appropriate value to achieve a concise output without cutting off essential information?

• A.Set max_tokens to 100
• B.Set max_tokens to 5
• C.Set max_tokens to 10
• D.Set max_tokens to 1000