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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with generating reproducible and consistent results for a particular GenAI model prompt during development and testing.
Which of the following is the primary model parameter to adjust in order to ensure that identical inputs produce identical outputs every time the model is run?

A) Max Tokens
B) Temperature
C) Top-p (Nucleus Sampling)
D) Random Seed

2. A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back-end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer's intent.
Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?

A) Utilize IBM watsonx's API orchestration layer to dynamically route requests to the correct system based on intent analysis.
B) Set up IBM watsonx to queue customer interactions and process them sequentially using a single system at a time.
C) Implement a monolithic architecture where all system interactions are handled within a single service.
D) Use synchronous REST APIs for all back-end systems, relying on watsonx's built-in intent recognition to manage API routing.

3. You are tasked with building a question-answering system using IBM WatsonX's LLM integrated with LangChain. The system needs to retrieve relevant documents from a corpus of research papers and generate accurate, contextually aware responses.
Which of the following steps is most critical when implementing a RAG pattern in this environment?

A) Using WatsonX LLM to pre-process the research papers before storing them in LangChain's memory.
B) Fine-tuning WatsonX's LLM on the research papers before enabling retrieval through LangChain.
C) Configuring LangChain to use WatsonX's LLM for exact keyword matching during document retrieval.
D) Setting up the vector database to store embeddings of the research papers for semantic search.

4. In a RAG system, the retriever is responsible for fetching relevant documents or information from a knowledge base based on the input query. Different retriever types can be used depending on the nature of the task.
Which retriever type is most suitable for a RAG system that requires efficient large-scale retrieval from a document corpus based on semantic similarity?

A) Lexical Retriever: Primarily returns results based on syntactic similarity, relying on word order and surface-level features of the input query.
B) Exact-Match Retriever: Returns documents based solely on keyword matching and is optimized for highly structured, labeled datasets.
C) Dense Retriever: Uses vector embeddings to retrieve documents based on the semantic similarity of the input query and stored documents.
D) Hybrid Retriever: Combines syntactic retrieval methods (like BM25) with semantic retrieval (like dense retrieval) but sacrifices retrieval speed for accuracy.

5. You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A) Perform a spelling correction on the entire dataset to remove any language inconsistencies.
B) Tokenize the dataset before curating it and mapping it to the taxonomy tree.
C) Manually categorize each interaction and organize them into a taxonomy tree structure.
D) Convert all text to lowercase to ensure uniformity in the dataset.

Solutions: