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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working on a project that involves deploying a series of prompt templates for a large language model on the IBM Watsonx platform. The team has requested a system that supports prompt versioning so that updates to the prompts can be tracked and tested over time.
Which of the following is the most important consideration when planning prompt versioning for deployment?

A) Version control should focus exclusively on the syntactical structure of the prompts, as changes to prompt content rarely impact the model's performance.
B) Prompts should be stored in a proprietary IBM format, as other formats are not compatible with the Watsonx platform when using versioning.
C) Each version of the prompt must have a unique identifier that can be referenced during model inference, to avoid conflicting results from different prompt versions.
D) The versioning system should automatically downgrade to the previous prompt version if the model returns a confidence score below a certain threshold during inference.

2. You are tasked with creating a prompt-tuned model using IBM watsonx.ai to enhance the quality of text generation for customer support. The goal is to fine-tune the model for improved context understanding based on specific customer queries.
Which of the following approaches would be the best method to initialize the prompt for tuning?

A) Use a pre-trained general-purpose prompt with no domain-specific customization
B) Use a prompt with pre-defined output patterns to restrict the model's possible responses
C) Use a manually crafted prompt tailored to the specific context of customer support queries
D) Construct a prompt using a large set of random tokens from the training corpus

3. When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?

A) Soft prompts produce more deterministic outputs because they are optimized for a specific task through manual input adjustment.
B) Soft prompts offer higher flexibility by dynamically adjusting the model's behavior based on pre-learned embeddings rather than relying on static instructions.
C) Soft prompts are preferable in scenarios where the task requires rigid structure and adherence to specific predefined guidelines.
D) Soft prompts allow for more human oversight by embedding specific instructions into the model's generation process.

4. While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

A) Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.
B) Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.
C) Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.
D) Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.

5. You are developing an AI-driven application using IBM watsonx and LangChain to automate legal document summarization for a law firm. The application needs to extract key legal points, summarize them, and generate insights from various sources, including external APIs, court databases, and private document repositories. You are tasked with creating a LangChain chain that integrates these sources, customizes prompt templates, and uses Large Language Models (LLMs) to provide legal summaries. The prompt template must allow for dynamic insertion of text from external sources and adapt based on the type of legal document.
Which LangChain chain design would best meet the needs of this application?

A) Design a ParallelChain where the text from different sources is processed in parallel by multiple LLMs, combining the results at the end.
B) Use a SequentialChain that first extracts text from external APIs and databases, processes it through custom prompt templates, and then sends the final processed text to an LLM.
C) Implement a SimpleChain that retrieves the required data from external APIs and directly sends the text to the LLM without prompt templates.
D) Employ a Retrieval-Augmented Generation (RAG) Chain, where the LLM queries external knowledge sources in real-time while applying a fixed prompt template.

Solutions: