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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with designing a LangChain-based AI workflow using watsonx.ai that incorporates multiple models for different tasks: document classification, entity extraction, and text generation. The final output should consist of a well-structured report that combines these processes.
What is the best strategy to orchestrate this workflow to ensure seamless integration of all tasks and a coherent final output?

A. Start with text generation using watsonx.ai and then refine the output through document classification and entity extraction.
B. Use document classification to filter the input data, followed by entity extraction to identify key terms, and then pass the refined information to watsonx.ai for text generation.
C. Run the document classification, entity extraction, and text generation tasks in parallel and merge the results at the end to produce the final report.
D. Build a modular workflow where each task operates independently without passing data between them, ensuring each task can be validated separately before generating the final report.

Question 2

You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A. Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
B. Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3
C. Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
D. Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6

Question 3

In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?

A. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
B. Embeddings are used to directly generate the textual responses in the output.
C. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.
D. Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.

Question 4

You are integrating watsonx.ai into an external system to handle text generation for a content creation application. The external system requires real-time processing and needs to interact with watsonx.ai frequently. Given this requirement, which integration method is most appropriate for ensuring reliable and scalable communication between the external system and watsonx.ai?

A. Use a REST API with synchronous requests, where the external system waits for watsonx.ai to respond before proceeding.
B. Implement Webhooks to receive updates from watsonx.ai when new data is generated and push it to the external system.
C. Integrate watsonx.ai through the SDK to directly embed AI capabilities into the external system, eliminating the need for API calls.
D. Leverage asynchronous REST API calls with callbacks to enable the external system to send requests and continue processing while waiting for the response.

Question 5

In the context of generative AI, you are tasked with optimizing a model's performance for a variety of use cases by tuning the prompts. One of your colleagues mentions using a "soft prompt" to improve the model's adaptability.
What best describes the difference between a hard prompt and a soft prompt?

A. A soft prompt is a fixed string of text used in fine-tuning, while a hard prompt adjusts dynamically based on input data.
B. Soft prompts are more readable and natural, whereas hard prompts consist of short, technical instructions.
C. Hard prompts are less efficient because they need to be re-trained with each task, while soft prompts are more versatile and adaptive across multiple tasks.
D. A hard prompt explicitly specifies all constraints, while a soft prompt relies on implicit learning from continuous inputs during training.

Solutions: