IBM C1000-185 Exam Questions : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are reviewing the results of a prompt-tuning experiment where the goal was to improve an LLM's ability to summarize technical documentation. Upon inspecting the experiment results, you notice that the model has a high recall but relatively low precision.
What does this likely indicate about the model's performance, and how should you approach further tuning?

A. The model is generating too many irrelevant details; focus on improving precision.
B. The model's summaries are incomplete, indicating poor understanding of the source material; consider fine-tuning the pre-trained embeddings.
C. The model's length of generated summaries is too short, indicating underfitting.
D. The model is overly conservative, missing relevant details; focus on improving recall.

Question 2

A team is using IBM InstructLab to customize a large language model (LLM) to automate responses in a healthcare chatbot application. The team wants to ensure the chatbot can handle user queries accurately, based on domain-specific instructions.
Which of the following correctly describes the role of the instruction optimization phase within the InstructLab workflow?

A. Instruction optimization involves retraining the model on a larger dataset for better accuracy.
B. Instruction optimization focuses on improving the dataset's quality by removing outliers and noise.
C. Instruction optimization refines prompts to improve the model's ability to follow task-specific instructions.

Question 3

You are tasked with creating structured prompts in IBM watsonx Prompt Lab to ensure consistency and reliability in generating responses from a machine learning model.
What is one major benefit of using structured prompts when experimenting with different prompts in Prompt Lab?

A. Structured prompts provide a template that ensures consistent intent, which can be adjusted with dynamic variables for flexibility.
B. Structured prompts ensure that the AI generates longer and more detailed responses by default.
C. Using structured prompts allows the AI to autonomously generate diverse outputs with minimal user input.
D. Using structured prompts makes AI responses deterministic, ensuring that no creativity is applied in the generated text.

Question 4

You are training a generative AI model using IBM's Tuning Studio and want to optimize its performance. You aim to avoid both overfitting and underfitting by carefully selecting the appropriate number of epochs.
Which of the following strategies would best help you set the optimal number of epochs during the tuning process?

A. Gradually increase the number of epochs until the loss on the training set reaches zero
B. Set the number of epochs equal to the size of the dataset
C. Set a high number of epochs and use early stopping to determine the optimal point
D. Use a single epoch to avoid overfitting

Question 5

In the context of decoding methods in IBM Watsonx Generative AI, both top-k and top-p sampling are used to control the output of the model.
Which of the following statements correctly distinguishes between top-k and top-p sampling?

A. Top-k sampling samples from a dynamic range of tokens, while top-p sampling restricts choices to a fixed number of tokens.
B. Top-p sampling allows for a more flexible token selection process based on cumulative probabilities, while top-k limits token selection to a fixed number of top choices.
C. Both top-k and top-p sampling ensure that no tokens are selected with a probability below the set threshold, regardless of their context.
D. Top-p sampling ensures deterministic outputs, whereas top-k sampling guarantees random selection from the entire vocabulary.

Solutions: