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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with developing a RAG system that integrates a transformer-based language model with a large document corpus. To speed up the development process, you are considering using specialized libraries designed for RAG.
Which of the following reasons best explains why these libraries are essential for your development process?

A) They allow for the retrieval of documents based purely on keyword search, optimizing for exact match over semantic similarity.
B) They streamline the integration of retrievers and generators, providing out-of-the-box support for embedding models and vector databases.
C) They provide a graphical interface for users to build RAG systems without requiring any programming knowledge.
D) They provide pre-trained retrieval models and generators, eliminating the need for any fine-tuning or customization of the system.

2. When working with IBM Watsonx Generative AI models, it's important to configure proper stopping criteria to control when the model should terminate the text generation process. You are developing a chatbot where responses should stay within a manageable length without losing coherence.
Which configuration best represents an effective stopping criterion to ensure coherent responses without abrupt truncation?

A) Greedy decoding with no stop sequence and maximum tokens set to 200.
B) Greedy decoding with temperature set to 2.0 and no stop sequence.
C) Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.
D) Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".

3. You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

A) Reduce memory utilization and maintain BLEU and ROUGE-L scores
B) Maximize BLEU score and reduce memory utilization
C) Increase memory utilization to reduce BLEU and ROUGE-L scores
D) Focus on improving the ROUGE-L score while increasing memory utilization

4. You are tasked with deploying a foundation model for text generation on the IBM Watsonx platform. The foundation model has been pre-trained on a large corpus but has not been fine-tuned for your specific use case.
What is the most critical factor to consider when deploying this model to ensure it performs optimally on the Watsonx platform?

A) You should enable quantization of the model to optimize inference performance without compromising accuracy in a production environment.
B) The model should be fine-tuned with domain-specific data before deployment, as Watsonx requires fine-tuning for all foundation models before they can be deployed.
C) You must ensure that the model is compatible with IBM's foundation model API, as Watsonx only allows deployment of models that integrate with its foundation model architecture.
D) The model's size should be reduced to fit within the memory constraints of the deployment environment, even if it leads to a loss of precision in its outputs.

5. In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

A) It reduces the time required to train models by lowering the accuracy threshold.
B) It allows for the precise allocation of computational resources to manage budget constraints.
C) It limits the number of model versions stored, improving system performance.
D) It provides real-time feedback on model deployment success rates.

Solutions: