IBM watsonx Generative AI Engineer - Associate : C1000-185

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are optimizing the generative AI model in IBM watsonx to balance creativity and coherence. You want to use a decoding method that dynamically adjusts the token probability threshold based on cumulative probabilities, thus ensuring the model generates coherent outputs while still allowing for some creativity.
Which parameter should you adjust, and what is the optimal setting?

A) Set temperature to 1.5 and top-p to 0.9
B) Set temperature to 1 and top-p to 0.2
C) Set temperature to 0 and top-p to 0.5
D) Set top-p to 0.9 and temperature to 0.7

2. You are developing a Retrieval-Augmented Generation (RAG) system to enhance the responses of a legal chatbot by integrating it with a vast legal document repository. You are using LangChain to build the pipeline, Watson ML for model hosting, and Elasticsearch as your document store.
What would be the most appropriate approach for combining these components into a RAG pipeline?

A) Use Elasticsearch for document retrieval -> Use LangChain to encode the documents -> Generate the response using Watson ML.
B) Use LangChain to chain together query encoding, document retrieval from Elasticsearch, and Watson ML for response generation.
C) Use Watson ML for document retrieval and response generation -> Use Elasticsearch to store model responses -> Use LangChain for chaining the responses together.
D) Use LangChain to pre-process documents -> Use Elasticsearch for model storage -> Use Watson ML to retrieve documents and generate responses.

3. In the context of analyzing prompt-tuning results, which statistical measure is most important to assess how well the tuned model generalizes to unseen data?

A) Number of epochs completed
B) Training loss
C) Validation loss
D) Accuracy on the training dataset

4. Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?

A) Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.
B) Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.
C) Soft prompts can increase the model's interpretability by providing clear, user-defined input instructions.
D) Soft prompts make it easier to control the model's behavior as the prompts are flexible and can be adjusted by the user during inference.

5. You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic-looking data without focusing on distribution conformity.
Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson-Darling test?

A) Bootstrapping Algorithm
B) Gaussian Mixture Models (GMMs)
C) Anderson-Darling Based Synthetic Data Generation (ADS-DG)
D) K-Nearest Neighbors (KNN)

Solutions: