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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A client is planning to deploy a Watsonx Generative AI model and has raised concerns about ethical usage, bias, and accountability in decision-making.
Which of the following is the most critical step to ensure AI governance during the deployment phase of the model?

A) Monitoring and auditing AI decisions for bias and fairness
B) Implementing a feedback loop for continuous model improvement
C) Testing the model's accuracy on a large set of random data
D) Training the model on additional data to improve accuracy

2. You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?

A) Set the temperature to 0.0
B) Decrease the temperature from 0.8 to 0.6
C) Decrease the temperature from 0.9 to 0.3
D) Increase the temperature from 0.5 to 1.5

3. You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A) Convert all text to lowercase to ensure uniformity in the dataset.
B) Perform a spelling correction on the entire dataset to remove any language inconsistencies.
C) Manually categorize each interaction and organize them into a taxonomy tree structure.
D) Tokenize the dataset before curating it and mapping it to the taxonomy tree.

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

5. In a Retrieval-Augmented Generation (RAG) setup, you notice that the model is generating responses that are not always relevant to the query, despite the knowledge base containing useful information.
What could be the most likely cause of this issue, and how should you address it?

A) The model is over-relying on the retrieval system and ignoring the language model's ability to generate coherent responses, so you should disable the retrieval component for general questions.
B) The knowledge base might contain outdated or irrelevant documents, so removing all non-recent documents would ensure the model generates more relevant responses.
C) The problem likely lies with the input format, so changing all queries to a pre-structured format (like templates) will ensure the retrieval and generation stages perform optimally.
D) The retrieval mechanism might be failing to fetch the most relevant documents from the knowledge base, so you should improve the search algorithm or use a better ranking system.

Solutions: