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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

A) Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])
B) Introduce explicit instructions in the prompt to avoid generating personal information
C) Increase the model's randomness by adjusting the temperature parameter
D) Reduce the model's token limit to restrict the amount of text generated

2. A developer is using a GitHub Code Retrieval API to help build a search engine that can locate relevant code snippets from public repositories. The API is designed to retrieve code based on the semantic similarity of the query (e.g., a description of what the code does) to the code itself.
What is the primary advantage of using a vector-based approach for code retrieval in this scenario?

A) It ensures that the code snippets returned are exact matches to the keywords in the query, avoiding irrelevant code.
B) It provides real-time updates to the code embeddings, ensuring the latest code is always retrieved.
C) It retrieves code snippets based on the semantic similarity between the query and the code, enabling the discovery of relevant code even when the query and code do not use the same keywords.
D) It speeds up retrieval by limiting the search to repositories where the user has committed code in the past.

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

4. 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 retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
B) 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.
C) 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.
D) 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.

5. You are tasked with generating synthetic data for fine-tuning a large language model (LLM) using the IBM Watsonx User Interface. You want to generate relevant training samples to improve the model's accuracy in a text classification task.
Which action should you prioritize to generate high-quality synthetic data using the interface?

A) Avoid using domain-specific prompts to keep the synthetic data unbiased and more generalizable.
B) Customize prompt templates to closely mimic the structure and format of the original training data.
C) Select only a few generic prompts to generate the largest volume of data possible, ensuring variety.
D) Choose a diverse set of prompts that span different domains unrelated to the original dataset.

Solutions: