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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?

A) Increase the number of positive sentiment examples in the dataset
B) Focus only on neutral and negative examples to challenge the model
C) Convert neutral examples into either positive or negative to simplify the task
D) Ensure an equal distribution of positive, negative, and neutral sentiment examples

2. You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?

A) Use a prompt that explicitly asks for neutrality across demographic groups.
B) Lower the temperature to reduce randomness in the model's response.
C) Switch to using deterministic (greedy) decoding to ensure more consistent outputs
D) Incorporate additional training data from underrepresented demographic groups.

3. In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?

A) Averaging the probabilities of multiple tokens at each step to generate a sequence
B) Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens
C) Selecting the token with the highest probability at each step, without considering future possibilities
D) Randomly selecting tokens from the top-k highest probability tokens at each step

4. How can developers best ensure that prompt templates are version-controlled and any changes to the templates can be tracked efficiently in a collaborative team environment?

A) Use a centralized cloud storage system that automatically saves all versions of the prompt templates
B) Implement a versioning system for prompt templates in a source control tool like Git
C) Update prompt templates directly in production and rely on error logs for tracking changes
D) Manually keep a changelog of prompt template modifications in a shared document

5. You are implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.
What is the most important function of LlamaIndex in managing document retrieval?

A) LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.
B) LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.
C) LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.
D) LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.

Solutions: