IBM C1000-185 : IBM watsonx Generative AI Engineer - Associate

You are enhancing an existing relational database system to support vector-based similarity search, integrating RAG into your infrastructure.
Which of the following technologies or approaches represents a valid method for extending a traditional SQL database to handle vector embeddings and similarity searches?

• A. Using graph database extensions to enable vector embeddings within a SQL database
• B. Normalizing vector embeddings into relational tables with foreign key constraints
• C. Using a full-text indexing engine, such as Elasticsearch, to store the vector embeddings
• D. Adding a plugin that provides support for k-nearest neighbors (k-NN) search

A generative AI model designed for healthcare content generation is being evaluated for ethical risks. The model tends to give preference to certain demographic groups when recommending treatments.
What is the most effective method to identify and mitigate this bias during the prompt engineering phase?

• A. Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
• B. Use adversarial debiasing techniques to adjust the model's internal representations during training.
• C. Limit the model's context window to prevent it from over-relying on demographic information.
• D. Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.

You are optimizing a generative AI model using prompts. You are tasked with choosing between a hard prompt and a soft prompt for generating a technical report.
Which option best describes a soft prompt in this context?

• A. A manually written prompt that is optimized using a grid search of the best keywords and patterns for the task.
• B. A prompt that restricts the model's output using hard-coded rules to ensure specific behavior during generation.
• C. A prompt that influences the model by embedding learned vectors into the input, modifying the generation behavior indirectly.
• D. A prompt that directly instructs the model with domain-specific keywords and structured language to steer the model's output.

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.

You are developing a generative AI application using LangChain, and you want the system to perform actions like searching a database or retrieving live web content based on a user's request.
How can you best incorporate tools in LangChain to enable the AI to perform such tasks autonomously?

• A. Build a LangChain chain that uses user inputs to sequentially call all the available tools and pick the one with the most relevant output.
• B. Configure LangChain to automatically load data from static sources based on historical query patterns, avoiding the need for dynamic tool selection.
• C. Rely on LangChain's memory module to remember previous user queries and provide real-time data access.
• D. Use a LangChain agent with a predefined set of tools to dynamically select and invoke the appropriate tool (e.g., database access, API call) based on the user's request.