IBM Certified watsonx Generative AI Engineer - Associate C1000-185

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IBM watsonx Generative AI Engineer - Associate C1000-185 Prüfungsfragen mit Lösungen:

1. You are implementing techniques to ensure that an IBM Watsonx Generative AI model does not expose any personal or sensitive information (PII) in its outputs.
What is the most effective technique for excluding personal information during the inference stage of the generative AI process?

A) Use temperature tuning to control the diversity of outputs, reducing the likelihood of personal information being revealed.
B) Implement real-time filtering of model outputs using regular expressions to detect and mask personal information.
C) Use a greedy decoding strategy to limit the creativity of the model and prevent unexpected outputs.
D) Train the model on synthetic data that does not include any personal information.

2. When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A) Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.
B) Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
C) Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.
D) Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.

3. You are optimizing a generative AI chatbot for concise responses to user queries, ensuring that it doesn't over-generate unnecessary content. However, you observe that the model occasionally stops prematurely, cutting off relevant information.
What configuration best addresses this issue without allowing for excessive output?

A) Stop Sequence = '.', Max Tokens = 500
B) Stop Sequence = '.', Max Tokens = 150
C) Stop Sequence = '</end>', Max Tokens = 250
D) Stop Sequence = '.' (period followed by a space), Max Tokens = 300

4. You are tasked with deploying a watsonx Generative AI solution for a client who requires real-time inference with minimal latency. The solution will be used for large-scale text generation tasks, where throughput is critical. The client also requires the system to handle fluctuating loads efficiently.
What would be the most appropriate deployment strategy?

A) Use a CPU-based deployment for cost efficiency, and manually adjust the number of instances as needed.
B) Deploy the model on a multi-node GPU cluster with Kubernetes to handle auto-scaling and fault tolerance.
C) Deploy the model in a hybrid architecture, splitting inference tasks between on-premise servers and cloud resources.
D) Deploy the model on a single GPU-based cloud instance with auto-scaling enabled.

5. In the context of generative AI optimization, you are tasked with improving the model's response accuracy across different domains. One suggestion is to use soft prompts for enhanced performance.
How does a soft prompt differ from a hard prompt in terms of implementation and flexibility?

A) Hard prompts are stored as vector embeddings within the model, while soft prompts are manually crafted by users to optimize generation.
B) Soft prompts are embedded during the model's training and rely on learned representations, while hard prompts are explicit text-based inputs given during inference.
C) Soft prompts use a rule-based system to guide the model, while hard prompts depend on statistical techniques to generate responses.
D) Soft prompts can dynamically change during inference based on the input data, whereas hard prompts are fixed throughout the entire generation process.

Fragen und Antworten: