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

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In the context of Generative AI (GenAI), various embedding models are used to represent textual data.
Which of the following best describes the difference between Word2Vec, BERT, and Sentence-BERT embedding models?

A) Word2Vec captures both word and sentence meanings in a single vector space, BERT generates only word embeddings, and Sentence-BERT generates embeddings for entire documents.
B) Word2Vec captures contextual relationships between words, while BERT and Sentence-BERT generate sentence-level embeddings based on the overall document length.
C) Word2Vec creates static word embeddings, BERT generates dynamic embeddings based on context, and Sentence-BERT produces embeddings specifically optimized for sentence-level tasks like semantic similarity.
D) Word2Vec uses a transformer architecture for embedding generation, whereas BERT and Sentence-BERT use neural networks to model context.

2. A company is considering using IBM Watsonx for two different use cases: (1) automating email responses for routine inquiries from customers, and (2) generating creative marketing copy for new product campaigns.
Which of the following best describes the model selection process for these use cases?

A) Choose a domain-specific model for automating email responses and a more generalized model with creative capabilities for generating marketing copy.
B) Use a large language model fine-tuned for email response generation for both tasks, as the fine-tuning process will enable the model to handle creative tasks as well.
C) Use the smallest possible model to reduce computational costs, regardless of the use case.
D) A single model should be used for both use cases since Watsonx can handle any type of text generation task equally well.

3. You are optimizing a generative AI model using prompts. You are tasked with choosing between a hard prompt and a soft prompt for generating a technical report.
Which option best describes a soft prompt in this context?

A) A manually written prompt that is optimized using a grid search of the best keywords and patterns for the task.
B) A prompt that restricts the model's output using hard-coded rules to ensure specific behavior during generation.
C) A prompt that influences the model by embedding learned vectors into the input, modifying the generation behavior indirectly.
D) A prompt that directly instructs the model with domain-specific keywords and structured language to steer the model's output.

4. You are optimizing a large language model (LLM) by prompt-tuning it for specific enterprise-level tasks. The goal is to initialize the prompt in such a way that it helps the model generalize well across various enterprise domains, such as finance, healthcare, and retail.
What is the most effective method to initialize the prompt for such a use case?

A) Start with a general prompt and gradually specialize it during fine-tuning
B) Use a short prompt that provides no guidance and allow the model to self-optimize
C) Initialize the prompt with an ensemble of prompts covering multiple domains
D) Use a single, highly specific prompt tailored to only one domain, such as finance

5. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
B) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
C) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
D) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
E) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.

Solutions: