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. You are designing a question-answering system that can provide responses based on a vast corpus of legal documents. Your solution leverages the Retrieval-Augmented Generation (RAG) pattern using an IBM watsonx-based architecture. The goal is to ensure that the system retrieves highly relevant documents and uses the retrieved content to generate accurate responses. The team is considering various libraries to integrate dense retrieval and generation models into this architecture.
Which of the following libraries or frameworks should you integrate to effectively implement the RAG pattern in this scenario, considering the need for both document retrieval and natural language generation?

A) PyTorch for implementing rule-based retrieval and generative model fine-tuning.
B) TensorFlow for dense retrieval and document embedding generation.
C) Keras for handling sparse retrieval and pre-trained generation models.
D) Hugging Face Transformers for language generation and FAISS for embedding-based document retrieval.

2. In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?

A) Quantization maintains model accuracy but doubles the computation required for inference.
B) Quantization always improves model performance but significantly increases model size.
C) Quantization eliminates the need for fine-tuning after deployment, ensuring zero accuracy loss.
D) Quantization reduces model size but may lead to a loss of accuracy, especially with aggressive quantization methods.

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

4. When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

A) It allows incremental training, saving computational resources by reusing checkpoints.
B) It expands the model's architecture to handle larger datasets.
C) It automatically increases the number of layers for more complex tasks.
D) It optimizes the model for multilingual capabilities by adding new embeddings.

5. In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

A) Reduce the model's token limit to restrict the amount of text generated
B) Introduce explicit instructions in the prompt to avoid generating personal information
C) Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])
D) Increase the model's randomness by adjusting the temperature parameter

Solutions: