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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are experimenting with a generative AI model to write a personalized email response template. You want to ensure that the output maintains a formal tone but occasionally produces creative phrasing without making nonsensical sentences. You are advised to adjust the top-p (nucleus sampling) parameter.
Which of the following settings would most effectively balance between formal coherence and occasional creativity in the generated output?

A) Set top-p to 0.0
B) Set top-p to 1.0
C) Set top-p to 0.95
D) Set top-p to 0.5

2. You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?

A) Increase the token limit to avoid premature cut-off
B) Use multiple stop sequences, including a period ."
C) Set the stop sequence to a punctuation mark like ";"
D) Use a more distinct and unlikely stop sequence, such as "<END>"

3. A data scientist is choosing between using hard prompts and soft prompts in a generative AI project.
Which of the following best explains why hard prompts might be more suitable for scenarios where explainability is crucial?

A) Hard prompts allow for a clear, human-readable set of instructions that directly guide the model's behavior.
B) Hard prompts are based on learned embeddings, which offer better model understanding due to their complexity.
C) Hard prompts dynamically adjust the model's internal representations, providing more clarity in complex situations.
D) Hard prompts reduce the model's flexibility by making the output deterministic, which enhances explainability.

4. When preparing a dataset for fine-tuning a large language model for a named entity recognition (NER) task, which of the following preprocessing steps is most critical for ensuring accurate entity classification?

A) Remove rare entities to improve model performance on common entities
B) Ensure proper tokenization of the dataset according to the model's vocabulary
C) Use sentence segmentation to isolate each named entity in its own sentence
D) Randomly shuffle the dataset before training to increase diversity

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

Solutions: