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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.
Which of the following roles is responsible for ensuring that the model is properly integrated into the client's existing systems and that data pipelines are established for continuous model improvement?

A) Data Scientist
B) DevOps Engineer
C) Solution Architect
D) Data Engineer

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

3. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
B) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
C) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
D) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
E) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.

4. You are developing a Retrieval-Augmented Generation (RAG) system to enhance the responses of a legal chatbot by integrating it with a vast legal document repository. You are using LangChain to build the pipeline, Watson ML for model hosting, and Elasticsearch as your document store.
What would be the most appropriate approach for combining these components into a RAG pipeline?

A) Use LangChain to chain together query encoding, document retrieval from Elasticsearch, and Watson ML for response generation.
B) Use LangChain to pre-process documents -> Use Elasticsearch for model storage -> Use Watson ML to retrieve documents and generate responses.
C) Use Watson ML for document retrieval and response generation -> Use Elasticsearch to store model responses -> Use LangChain for chaining the responses together.
D) Use Elasticsearch for document retrieval -> Use LangChain to encode the documents -> Generate the response using Watson ML.

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

Solutions: