IBM watsonx Generative AI Engineer - Associate : C1000-185

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are using the IBM watsonx platform to fine-tune an existing generative AI model for a text-based customer support system. Due to privacy constraints, the company cannot use real customer data, so synthetic data must be generated using the platform's UI. Your task is to create and configure the synthetic data and fine-tune the model to optimize customer query handling.
Which of the following actions will best ensure the synthetic data generation process leads to a well-tuned model that handles diverse customer queries effectively? (Select two)

A) Prioritize the generation of synthetically perfect customer queries with no grammatical errors to ensure high-quality data.
B) Simulate both simple and complex customer queries, including ambiguous or vague requests, in the synthetic data.
C) Generate a synthetic dataset that only covers the most frequent customer queries to reduce complexity in model training.
D) Leverage the user interface to create synthetic data that includes rare edge cases, such as technical support questions or multi-part inquiries.
E) Fine-tune the model immediately after generating synthetic data, without further inspection, to maintain efficiency.

2. You have been using a pre-trained foundation model for a financial text summarization application. While the model is generating summaries that are generally accurate, it sometimes fails to handle domain-specific financial jargon. You are considering whether it's time to tune the model to optimize its performance for this task.
Which of the following conditions would most strongly justify tuning the foundation model for your specific use case?

A) The model produces a high number of tokens in each output, which increases the cost of usage.
B) The model's accuracy fluctuates based on the length of the input text.
C) The model exhibits acceptable performance but occasionally generates off-topic responses unrelated to financial data.
D) The model generates output that is highly relevant to general topics but often misinterprets industry-specific terms like "leverage" or "derivative."

3. Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?

A) Quantization-aware training (QAT)
B) Post-training static quantization
C) Hybrid quantization
D) Post-training dynamic quantization

4. You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

A) Fine-tune IBM InstructLab itself to generate data that closely resembles the training data format, ensuring consistent sentiment distribution.
B) Use IBM InstructLab to generate synthetic data, but only for neutral sentiment, as the model already handles positive and negative sentiment well.
C) Use a generic prompt to generate a wide variety of data from IBM InstructLab, regardless of sentiment polarity.
D) Increase the diversity of synthetic data by focusing on outliers and rare sentiment cases that are underrepresented in the original dataset.

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

Solutions: