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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are designing a prompt for a generative AI model in a customer support chatbot. The goal is to help users troubleshoot issues with a software installation.
Which of the following prompts would be the most effective for guiding the AI to deliver clear, actionable troubleshooting steps?

A. "Please list all the possible errors that occur during software installation."
B. "Tell the user what software installation is and how it works."
C. "Guide the user through troubleshooting software installation issues, starting from common problems like insufficient disk space and incompatible OS."
D. "Can you tell the user about software installation?"

Question 2

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. Solution Architect
B. Data Engineer
C. DevOps Engineer
D. Data Scientist

Question 3

You are tasked with building a Retrieval-Augmented Generation (RAG) system using Elasticsearch for document storage, Watson ML for model hosting, and LangChain for orchestration. The chatbot is supposed to query a large database of medical records and generate responses based on the retrieved information. During testing, you notice that irrelevant documents are often retrieved, leading to low-quality responses.
What would be the best approach to improve document relevance in this RAG setup?

A. Fine-tune the Watson ML model on a dataset containing both queries and relevant documents, then update Elasticsearch with the model outputs.
B. Implement a BM25 similarity algorithm in Elasticsearch to improve document retrieval.
C. Pre-process the medical records in LangChain to remove irrelevant information before sending the query to Elasticsearch.
D. Update the Elasticsearch index by re-indexing documents using embeddings from the Watson ML model.

Question 4

In the context of prompt engineering, how does the repetition penalty parameter influence the behavior of a generative AI model, and which of the following scenarios demonstrates its correct usage?

A. Setting the repetition penalty to 1.0 disables any influence on repetition and allows the model to generate text without penalizing previously used tokens
B. A repetition penalty higher than 1.0 will discourage the model from repeating the same words or phrases, while a value lower than 1.0 encourages repetition
C. A repetition penalty of 1.0 encourages the model to introduce more varied word choices in its output
D. A repetition penalty of 0.5 is ideal for generating creative text, as it promotes repetition of key phrases for emphasis

Question 5

You are part of a team building an AI-powered assistant that helps software developers by answering technical queries. To handle the vast amount of technical documentation efficiently, the team has chosen to implement the Retrieval-Augmented Generation (RAG) pattern. LangChain is used to construct the retrieval-generation pipeline, and SingleStore is employed to store the document embeddings. You need to ensure that the integration of LangChain with SingleStore allows for real-time retrieval of relevant technical documentation.
Which of the following configurations should be used to ensure an optimal RAG implementation with LangChain and SingleStore?

A. Use LangChain to embed both queries and documents into the same vector space and store these embeddings in SingleStore. During inference, retrieve documents using SQL queries based on keyword similarity.
B. Use LangChain to retrieve pre-generated embeddings from SingleStore, and then fine-tune the generative model based on the retrieved embeddings to improve response accuracy.
C. Use LangChain to retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
D. Use LangChain to embed documents into a vector space and store these embeddings in SingleStore. During inference, embed the query in the same vector space and retrieve documents using vector similarity search.

Solutions: