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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with optimizing a large language model (LLM) for deployment in a resource-constrained environment where memory usage and computational cost need to be minimized without significantly compromising model accuracy.
Which quantization technique would be the most appropriate to achieve this balance?

A) Post-training weight clustering
B) Full integer quantization with 8-bit precision
C) Post-training dynamic quantization
D) Mixed-precision floating-point quantization

2. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.
B) Lower the model's perplexity score to improve both completeness and factual accuracy.
C) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
D) Increase the length of the input prompt to ensure that responses are more complete.

3. In the context of prompt engineering for IBM Watsonx Generative AI, which of the following is the most accurate description of a prompt variable?

A) A prompt variable is a function that allows real-time feedback from the model to modify the prompt after generation.
B) A prompt variable is a fixed string that the AI uses to refine its generative process for more context-aware responses.
C) A prompt variable is a placeholder within a prompt template that can be replaced with specific input values during execution.
D) A prompt variable is a predefined input that alters the architecture of the AI model during runtime.

4. You are tasked with optimizing a generative AI model in IBM watsonx.ai for an NLP-based application.
During the planning stage, which of the following data elements is most important to ensure the model generalizes well to real-world application usage?

A) High-dimensional feature vectors from a small, well-curated dataset
B) Pre-processed data with stop words removed to reduce noise
C) A large, diverse dataset with both structured and unstructured data
D) Only the structured data relevant to specific business rules

5. You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A) "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."
B) "Generate a summary for [New Contract]."
C) "Example 1: [First Contract]
D) "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."

Solutions: