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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are working with a foundation model pre-trained on a large general-purpose dataset, and you plan to deploy it for a specialized task in healthcare-related text generation. However, before tuning the model, you want to assess whether tuning is necessary for your use case.
Which of the following is the best indicator that it is time to tune the foundation model for your task?

A. The model's inference time is longer than expected, and you need to reduce latency for real-time applications.
B. The model performs well on general datasets but fails to capture specific domain-related terminology and context.
C. You are noticing that the model occasionally makes grammar mistakes in the generated text.
D. The model's accuracy is already above 90%, but you want to achieve 95% accuracy for your task.

Question 2

You are tasked with integrating watsonx.ai into a legacy system that operates over HTTP and requires strict security and monitoring of the API calls. The legacy system lacks modern authentication mechanisms like OAuth.
Which integration method would best suit the needs of this environment while ensuring security and efficient API management?

A. Implement a GraphQL API to allow the legacy system to query specific fields and reduce payload size, thereby improving efficiency.
B. Use REST API with API key-based authentication, leveraging HTTPS for encrypted communication between the legacy system and watsonx.ai.
C. Directly embed the watsonx.ai SDK into the legacy system to handle API management and security, eliminating the need for external API calls.
D. Use a REST API with OAuth authentication and token management to secure the communication.

Question 3

You are designing a document search application using IBM Watson's RAG architecture. You need to generate vector embeddings for your document corpus, which consists of unstructured text data. The vector embeddings will be used for similarity search in conjunction with a retriever model.
Which of the following steps is a prerequisite to generating effective vector embeddings from the unstructured text using an embedding API?

A. Preprocessing the text by tokenizing it into individual words and removing stop words
B. Choosing a transformer-based model pre-trained on a domain-specific corpus to generate the vector embeddings
C. Compressing the text data to reduce its dimensionality before passing it to the embedding API
D. Ensuring that the text data is fully normalized, including removing punctuation and converting all text to lowercase

Question 4

You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits.
Which configuration would help minimize generation costs while ensuring an accurate summary of the document?

A. Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.
B. Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed
C. Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.
D. Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.

Question 5

You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A. Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
B. Implement a post-processing filter to remove any potentially offensive or legally sensitive content.
C. Use a comprehensive training dataset that includes diverse business domains to reduce biases.
D. Include fairness metrics in the model evaluation stage to monitor for biased outputs.

Solutions: