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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Which of the following represents the most effective use of example input prompts within IBM Watsonx's Prompt Lab for generating a high-quality response?

A) Crafting prompts with very specific and detailed instructions, ensuring the model follows a strict framework for the desired response.
B) Using prompts with complex sentence structures and advanced terminology to challenge the model's understanding capabilities.
C) Writing prompts that are extremely vague, allowing the model to freely interpret the input and generate diverse responses.
D) Using example prompts that introduce multiple topics at once to evaluate how well the model handles multitasking during response generation.

2. IBM Watsonx's Prompt Lab offers various options to refine prompts for generating more effective AI outputs.
Which of the following is an accurate description of an editing option available in Prompt Lab?

A) Users can apply real-time machine learning to modify the underlying model parameters within Prompt Lab.
B) Users can use Prompt Lab to train the AI model on new datasets and retrain it based on prompt performance.
C) Users can disable the model's access to certain pre-trained knowledge domains within Prompt Lab to focus its output on specific areas.
D) Prompt Lab allows users to experiment with prompt structures, such as adjusting token limits or adding contextual instructions, to improve responses.

3. Which of the following best describes the difference between a hard prompt and a soft prompt in the context of generative AI optimization?

A) A hard prompt is used for deterministic output, while a soft prompt allows for random sampling.
B) A hard prompt consists of static, predefined instructions, while a soft prompt is optimized using continuous input embeddings that adapt based on the training context.
C) A hard prompt directly modifies the model architecture, while a soft prompt changes the model's training data.
D) A hard prompt encodes the model's hyperparameters, while a soft prompt adjusts the learning rate dynamically.

4. You are fine-tuning the output behavior of a generative AI model in IBM Watsonx for creative content generation. You decide to adjust the temperature parameter to influence the randomness of the model's output.
Which of the following best describes the effect of increasing the temperature value?

A) Raising the temperature encourages the model to consider less likely tokens, leading to more diverse and creative outputs.
B) Raising the temperature makes the model more likely to repeat tokens, reducing variability in its responses.
C) Increasing the temperature makes the model generate more deterministic responses by always selecting the most probable token at each step.
D) A higher temperature setting reduces the length of the generated output by limiting the number of tokens in each response.

5. You are working on a Retrieval-Augmented Generation (RAG) system using IBM watsonx. The system needs to retrieve relevant documents based on a user's query and generate a response using a language model. To optimize retrieval, you are tasked with generating vector embeddings for documents and queries using a pre-trained model. Your goal is to ensure that the embeddings are semantically meaningful to improve the retrieval accuracy.
Which of the following steps should be taken to ensure the vector embeddings are correctly generated and effective for document retrieval in a RAG system? (Select two)

A) Normalize the vector embeddings after generation to ensure they are comparable during retrieval.
B) Generate embeddings for documents only and skip embeddings for user queries, relying on traditional keyword-based retrieval for queries.
C) Use a pre-trained model designed specifically for embedding generation rather than general-purpose language models.
D) Use a generative language model to generate embeddings without any fine-tuning, as it captures all the necessary context.
E) Manually adjust the embedding vectors to emphasize certain keywords that are more important for retrieval.

Solutions: