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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with creating a prompt-tuned model using IBM watsonx.ai to enhance the quality of text generation for customer support. The goal is to fine-tune the model for improved context understanding based on specific customer queries.
Which of the following approaches would be the best method to initialize the prompt for tuning?

A) Construct a prompt using a large set of random tokens from the training corpus
B) Use a manually crafted prompt tailored to the specific context of customer support queries
C) Use a pre-trained general-purpose prompt with no domain-specific customization
D) Use a prompt with pre-defined output patterns to restrict the model's possible responses

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

3. What is a key advantage of using prompt variables in IBM Watsonx for a chatbot application that needs to handle multiple user intents?

A) Using prompt variables ensures that the model will always select the most relevant response based on past conversations.
B) Prompt variables allow the model to automatically detect the user's intent, ensuring accurate responses.
C) Prompt variables enable developers to predefine multiple static responses for each possible user input.
D) Prompt variables allow for the dynamic injection of user-specific details into the response, improving personalization.

4. You are tasked with deploying two generative AI systems: one for document summarization and another for question-and-answer (Q&A). Both systems need to handle large volumes of unstructured text, but they differ in their response time requirements and interaction with users.
Which deployment strategy would be most appropriate for this scenario?

A) Deploy the summarization model as a batch processing system, while the Q&A system is deployed as an on-demand service using a low-latency inference model.
B) Use a single deployment strategy where both the summarization and Q&A systems are integrated into a shared model that processes all requests through the same inference pipeline.
C) Deploy both systems as a single pipeline, with the summarization model providing input to the Q&A model to improve the quality of answers.
D) Use a rule-based model for summarization and a machine learning-based model for Q&A, combining them into a single service endpoint for deployment.

5. You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?

A) Delete all rows containing null values in the 'ticket_description' field.
B) Use Data Refinery's "Data Quality" feature to generate a profiling report and assess the scope of the issues.
C) Proceed with training the model without any changes, as missing data will not impact NLP models significantly.
D) Replace all null values with zeros, as this will standardize the dataset.

Solutions: