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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?

A) Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.
B) Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.
C) Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.
D) Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.

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

3. You are tasked with building a Retrieval-Augmented Generation (RAG) system for answering legal questions. The legal documents vary significantly in complexity and structure.
How would you optimize embeddings in this domain to ensure the system retrieves the most relevant documents? (Select two)

A) Train a domain-specific embedding model using legal documents to better capture the nuances of legal terminology.
B) Integrate additional metadata (e.g., document date, author) into the embedding representation to improve retrieval.
C) Use an unsupervised learning approach to generate embeddings, as labeled data is not necessary for improving retrieval performance.
D) Rely solely on word-level embeddings to capture the meaning of legal phrases and concepts.
E) Apply dimensionality reduction techniques like PCA to compress embeddings and improve retrieval speed.

4. You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?

A) Use a prompt that explicitly asks for neutrality across demographic groups.
B) Incorporate additional training data from underrepresented demographic groups.
C) Lower the temperature to reduce randomness in the model's response.
D) Switch to using deterministic (greedy) decoding to ensure more consistent outputs

5. Which of the following techniques is the most effective for reducing bias in generative AI models through prompt engineering?

A) Using neutral and carefully phrased prompts to avoid triggering biased outputs
B) Applying Greedy Decoding to ensure the most likely tokens are selected during generation
C) Fine-tuning the model using training data that explicitly includes examples of biased outputs
D) Allowing the model to auto-correct its own responses by cross-referencing with other outputs

Solutions: