IBM watsonx Generative AI Engineer - Associate : C1000-185

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A) Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3
B) Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
C) Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
D) Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6

2. You are designing a generative AI model to generate detailed technical

A) Stop Sequence = '<END>', Max Tokens = 100
B) Stop Sequence = None, Max Tokens = 700
C) Max Tokens = 500: This provides enough space for a detailed technical
D) Stop Sequence = 'END', Max Tokens = 500

3. In the IBM watsonx Prompt Lab, you are tasked with improving a customer-facing chat model by editing and refining prompts.
Which of the following is an effective prompt editing option to fine-tune a chat-based generative AI model?

A) Decreasing the number of tokens allowed in the output to encourage more creative responses.
B) Modifying the stop sequence to ensure the model responds with shorter answers for concise customer interactions.
C) Increasing the temperature parameter to make the model more deterministic and consistent in its responses.
D) Altering the tone of the prompt to make it more aligned with the brand's voice and customer expectations.

4. Your team is responsible for deploying a generative AI system that will interact with customers through automated chatbots. To improve the quality and consistency of responses across different queries and customer profiles, the team has developed several prompt templates. These templates aim to standardize input to the model, ensuring that outputs are aligned with business objectives. However, the team is debating whether using these prompt templates will provide tangible benefits in the deployment.
What is the primary benefit of deploying prompt templates in this AI system?

A) Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.
B) Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.
C) Enhancing the model's ability to generalize across unseen data by training it specifically on the variations included in the prompt template.
D) Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.

5. In the context of a Retrieval-Augmented Generation (RAG) system using IBM Watsonx, which of the following is the correct process for generating vector embeddings for document retrieval?

A) Tokenize the text data, then pass the tokens through a pre-trained language model to generate vector embeddings for each token, combining them into a final vector.
B) Directly extract the vector embeddings from raw text without any tokenization or processing steps by passing the raw data to a pre-trained language model.
C) Use a pre-trained generative model to generate text predictions, then use these predicted tokens as the basis for vector embedding generation.
D) Load the text data into the model, tokenize it, and directly feed the tokens into a decoder for vector generation.

Solutions: