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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?

A) Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.
B) Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.
C) Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.
D) Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.

2. You are tasked with fine-tuning a language model using a prompt-tuning approach on a dataset consisting of customer service chat logs. The goal is to optimize the model's ability to generate polite and contextually appropriate responses.
Which of the following steps are essential when preparing the dataset for prompt-tuning in this context? (Select two)

A) Ensure all examples in the dataset follow the exact same input-output format.
B) Remove any conversations that contain excessive user slang or misspellings.
C) Convert all user queries into lowercase to reduce noise in the dataset.
D) Ensure each conversation includes both customer input and agent response as context for the model.
E) Separate the dataset into training, validation, and test subsets.

3. You are working on generating synthetic training data using IBM InstructLab to supplement a small dataset for a question-answering system.
Which strategy would most effectively enhance the dataset without introducing biases or artifacts?

A) Generate a large amount of synthetic data by directly feeding the model with random prompts, ensuring data diversity.
B) Manually tweak each generated response to ensure it's free of errors and aligns with the intended task.
C) Automatically generate synthetic data using a different model architecture than the one being fine-tuned.
D) Use prompts that closely mimic the structure and semantics of the real dataset's questions to maintain consistency.

4. You are designing a document search application using IBM Watson's RAG architecture. You need to generate vector embeddings for your document corpus, which consists of unstructured text data. The vector embeddings will be used for similarity search in conjunction with a retriever model.
Which of the following steps is a prerequisite to generating effective vector embeddings from the unstructured text using an embedding API?

A) Choosing a transformer-based model pre-trained on a domain-specific corpus to generate the vector embeddings
B) Preprocessing the text by tokenizing it into individual words and removing stop words
C) Ensuring that the text data is fully normalized, including removing punctuation and converting all text to lowercase
D) Compressing the text data to reduce its dimensionality before passing it to the embedding API

5. In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few-shot prompting techniques.
Which of the following best explains the key difference between zero-shot and few-shot prompting?

A) Zero-shot prompting does not use any examples in the input prompt, while few-shot prompting includes a few examples to guide the model.
B) Zero-shot prompting provides the model with a few example tasks to help it understand the problem, while few-shot prompting provides no examples at all.
C) In zero-shot prompting, the model learns from a large number of examples during the inference stage, while in few-shot prompting, only a single example is used.
D) Few-shot prompting is used only for training the model, while zero-shot prompting is used only for inference tasks.

Solutions: