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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A) Implement a post-processing filter to remove any potentially offensive or legally sensitive content.
B) Include fairness metrics in the model evaluation stage to monitor for biased outputs.
C) Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
D) Use a comprehensive training dataset that includes diverse business domains to reduce biases.

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

3. While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

A) Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.
B) Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
C) Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.
D) Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.

4. You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?

A) Increase the number of training epochs to further refine the model
B) Apply model quantization to reduce the complexity of the output
C) Use a higher learning rate to improve model convergence
D) Analyze token distribution patterns in the generated outputs

5. You are tasked with designing a prompt for a sentiment analysis model based on a large language model (LLM). The goal is to generate a coherent response from the model that aligns with a particular sentiment (positive, negative, or neutral) for customer reviews of a product.
Which of the following prompt designs are best suited to generate a positive review response? (Select two)

A) "Write a review about the product that highlights both its pros and cons."
B) "Write a neutral review, neither praising nor criticizing the product."
C) "Describe the product as if you were a very satisfied customer, and you were recommending it to a friend."
D) "Generate a positive review about the product, focusing on the key strengths and avoiding any negative aspects."
E) "Analyze the product based on the customer feedback and write a review that covers all sentiments."

Solutions: