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IBM watsonx Generative AI Engineer - Associate Sample Questions:
1. You are tasked with developing a system that uses a vector database to store embeddings generated from a large corpus of documents. The system should be able to perform fast and efficient nearest neighbor search while balancing accuracy and speed. Given the large volume of data and the need for scalability, you are considering different indexing strategies offered by vector databases.
Which of the following indexing techniques is the most appropriate for balancing search accuracy and speed in high-dimensional vector space, and why?
A) Exact nearest neighbor (ENN) search using KD-trees.
B) Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.
C) Rely on full-text indexing of documents and avoid vector search altogether.
D) Linear search over the entire vector dataset.
2. When preparing a dataset for fine-tuning a large language model for a named entity recognition (NER) task, which of the following preprocessing steps is most critical for ensuring accurate entity classification?
A) Randomly shuffle the dataset before training to increase diversity
B) Remove rare entities to improve model performance on common entities
C) Use sentence segmentation to isolate each named entity in its own sentence
D) Ensure proper tokenization of the dataset according to the model's vocabulary
3. You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?
A) Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6
B) Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
C) Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3
D) Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
4. You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?
A) Perplexity score
B) Token generation speed
C) Log-likelihood of generated responses
D) BLEU score
5. You are designing prompt templates for an automated content generation tool that your team uses for marketing copy. The tool must balance cost efficiency with the generation of high-quality outputs. The prompts should be structured to avoid excessive token usage while ensuring that the model provides coherent and actionable content.
Which of the following techniques would best optimize the design of cost-effective prompt templates?
A) Designing prompts that incorporate redundancy to ensure model outputs address all possible user needs.
B) Embedding explicit stop words within the prompt to control the length of generated outputs.
C) Using placeholders in the prompt templates for dynamic insertion of variables specific to each task.
D) Creating templates that provide extensive background information to give the model context.
Solutions:
Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: C |