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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Your team is tasked with enhancing a document search engine using IBM watsonx Discovery. The search engine will be used to help employees quickly find relevant internal documentation, such as policy guides, project specifications, and technical manuals. You have decided to use Retrieval-Augmented Generation (RAG) to enhance this search engine's performance. The current focus is on selecting the best retriever for the task and setting up a vector database to store document embeddings.
Which of the following retriever types is most suitable for this RAG-based search engine setup, considering the diverse and unstructured nature of the documents, and why?

A) Sparse retriever with BM25 ranking in combination with a traditional relational database.
B) Rule-based retriever using manually crafted rules to find documents based on predefined logic.
C) Sparse retriever with BM25 ranking in combination with a traditional relational database.
D) Hybrid retriever that combines sparse retrieval methods with dense embeddings stored in a flat-file system.

2. In IBM Watsonx's Prompt Lab, example input prompts can be used to improve the effectiveness of generated responses.
Which of the following best describes a key benefit of utilizing example input prompts in Prompt Lab?

A) Example input prompts guarantee consistency across all outputs, regardless of variability in user-provided data.
B) Example input prompts automatically adjust the model's training dataset for more accurate predictions.
C) Example input prompts allow the model to learn new concepts and update its knowledge base dynamically.
D) Example input prompts help generate responses that are more aligned with the specific context or style intended by the user.

3. You are tasked with preparing a dataset for training a machine learning model using IBM Watsonx. The dataset contains over 1 million rows, and you notice a significant imbalance in the distribution of class labels.
To optimize model performance and minimize bias, what would be the best next step in addressing this imbalance?

A) Remove the majority class instances to balance the dataset.
B) Randomly shuffle the data to improve the model's exposure to different instances during training.
C) Increase the learning rate of the model to improve its ability to learn from the imbalanced data.
D) Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic data for the minority class.

4. You are building a RAG system that integrates LlamaIndex for efficient document retrieval from a knowledge base containing millions of documents.
Which of the following is the most critical consideration for ensuring the system performs well at scale?

A) Adjusting LlamaIndex to prioritize documents that contain certain predefined keywords, optimizing retrieval for specific business terms.
B) Configuring LlamaIndex to perform real-time indexing of incoming data streams to ensure the most up-to-date documents are always retrieved.
C) Setting LlamaIndex to index only a small subset of the documents to minimize the time it takes to retrieve any relevant document.
D) Ensuring LlamaIndex generates unique embedding vectors for each document and storing them in an optimized vector database for quick lookups.

5. You are tasked with configuring a generative model to assist legal professionals by generating contract clauses. The model should produce complete, contextually relevant clauses but should avoid overly long and redundant text. You must ensure that the generation process stops appropriately once a clause is completed.
Which of the following strategies is the most appropriate for configuring stopping criteria?

A) Use a stop sequence based on legal clause delimiters and impose a minimum token limit of 50.
B) Set the top-k value to 1 and use the default maximum token limit, relying on the model's internal stopping criteria.
C) Set a maximum token limit of 500 and implement a fixed length stopping criterion.
D) Use a beam search algorithm and impose a strict length restriction of 100 tokens.

Solutions: