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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with fine-tuning a large language model (LLM) to perform sentiment analysis on product reviews. The dataset contains customer reviews, but some reviews are very short, and others contain irrelevant data like product specifications or spam. You want to prepare the dataset for fine-tuning by ensuring the data is clean, relevant, and representative of the task at hand.
Which of the following steps is most critical to ensure the dataset is suitable for fine-tuning?

A) Filter the dataset to remove irrelevant or outlier reviews
B) Increase the learning rate to adjust for inconsistencies in the dataset
C) Randomly remove 10% of the dataset to reduce noise
D) Increase the number of epochs to handle the noisy data

2. Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

A) To generate new responses based on training data, without relying on external data sources.
B) To perform entity recognition and classify documents based on specific keywords, ignoring the overall document meaning.
C) To return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
D) To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.

3. In a generative AI model, you are tasked with producing creative yet coherent text for a marketing campaign. You want to ensure that the output contains varied word choices and diverse sentence structures while still maintaining some degree of logical consistency.
Which of the following settings for the temperature parameter would most likely achieve this balance?

A) Temperature = 2.0
B) Temperature = 0.0
C) Temperature = 0.2
D) Temperature = 0.7

4. When working with IBM Watsonx Generative AI models, it's important to configure proper stopping criteria to control when the model should terminate the text generation process. You are developing a chatbot where responses should stay within a manageable length without losing coherence.
Which configuration best represents an effective stopping criterion to ensure coherent responses without abrupt truncation?

A) Greedy decoding with temperature set to 2.0 and no stop sequence.
B) Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.
C) Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".
D) Greedy decoding with no stop sequence and maximum tokens set to 200.

5. In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?

A) Retraining the model based on emerging trends in data
B) Initial testing on synthetic datasets and model validation
C) Deployment to production with regular monitoring and logging
D) Iterative prompt tuning based on A/B test results and feedback loops

Solutions: