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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In the context of IBM Watsonx Generative AI models, hallucinations refer to outputs where the model generates text that is factually incorrect or not grounded in the provided input or training data. Understanding the underlying causes of hallucinations is critical for maintaining the reliability of the model.
Which of the following best describes a primary cause of hallucinations in generative models?

A) The model's incapacity to follow the temperature parameter settings.
B) The model's training on incomplete or unstructured datasets leading to incorrect generalizations.
C) The model's use of a greedy decoding strategy without beam search.
D) The model's over-reliance on token repetition to form coherent sentences.

2. You are managing a generative AI model deployment in IBM Watsonx and need to implement prompt versioning to ensure traceability and reproducibility of model behavior over time.
Which of the following strategies best enables versioning of prompts during deployment?

A) Using a source control system (e.g., Git) to track prompt changes alongside model code.
B) Relying on model checkpointing to manage both model weights and prompts.
C) Storing prompts in a flat file system and manually tracking versions.
D) Disabling versioning for prompts since it is not required for generative models.

3. Prompt Lab in IBM Watsonx Generative AI offers several advantages for AI prompt engineering.
Which of the following best describes a primary benefit of using the Prompt Lab feature?

A) It allows users to design custom AI models from scratch to handle specific tasks.
B) It provides a collaborative environment where multiple users can co-author prompts in real time.
C) It enables users to test different versions of prompts and receive immediate feedback on their effectiveness.
D) It guarantees that all generated responses adhere to industry-specific regulatory standards.

4. You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai's large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents.
What is the best approach to achieve this integration?

A) Use Watson Discovery for summarizing documents and watsonx.ai's LLM for only retrieving relevant content from the documents.
B) Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai's LLM for summarization through API calls.
C) Use watsonx.ai's LLM to both retrieve and summarize the documents, bypassing Watson Discovery.
D) Use watsonx.ai's LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.

5. A client needs a Generative AI solution to summarize large legal documents into concise briefs. The solution must capture the critical legal arguments while preserving the formal language required in legal contexts. Additionally, the client wants the model to identify key legal clauses and ensure their inclusion in the summaries. You have a pre-trained LLM that was trained on general text, and now you must design a generative solution to meet the client's needs.
What would be your next step in analyzing and designing the most effective solution?

A) Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.
B) Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.
C) Apply model quantization to optimize the LLM for handling long legal documents more efficiently.
D) Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.

Solutions: