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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q255-Q260):

NEW QUESTION # 255
You are integrating watsonx.ai into an external system to handle text generation for a content creation application. The external system requires real-time processing and needs to interact with watsonx.ai frequently. Given this requirement, which integration method is most appropriate for ensuring reliable and scalable communication between the external system and watsonx.ai?

A. Implement Webhooks to receive updates from watsonx.ai when new data is generated and push it to the external system.
B. Use a REST API with synchronous requests, where the external system waits for watsonx.ai to respond before proceeding.
C. Integrate watsonx.ai through the SDK to directly embed AI capabilities into the external system, eliminating the need for API calls.
D. Leverage asynchronous REST API calls with callbacks to enable the external system to send requests and continue processing while waiting for the response.

Answer: D

NEW QUESTION # 256
You are developing a chatbot application that uses IBM watsonx to assist users with customer support. The chatbot needs to respond with accurate, up-to-date information from a large corpus of documents. The documents include unstructured data, such as support tickets, product guides, and troubleshooting steps. Due to the need for highly specific answers, the system should be able to retrieve relevant information from the document base, while also generating human-like responses using a large language model (LLM).
In this scenario, which configuration should be used to ensure the chatbot retrieves the most relevant context before generating a response, and why would this approach be beneficial?

A. Use a knowledge graph-based retriever integrated with the LLM, leveraging structured relationships between data points.
B. Use a term-based retriever with an inverted index and perform keyword-based search, followed by minimal model tuning for response generation.
C. Use a dense retriever with a vector database for embedding-based retrieval, followed by using the LLM for context generation.
D. Use a sparse retriever and a standard SQL database, then fine-tune the language model to perform context matching.

Answer: C

NEW QUESTION # 257
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. Ensuring that the text data is fully normalized, including removing punctuation and converting all text to lowercase
C. Preprocessing the text by tokenizing it into individual words and removing stop words
D. Compressing the text data to reduce its dimensionality before passing it to the embedding API

Answer: A

NEW QUESTION # 258
Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

A. Use adversarial training techniques to make the model more robust against bias and poisoning.
B. Fine-tune the model using only the training data from trusted sources, without expanding the dataset.
C. Apply prompt engineering techniques to avoid triggering known biases in the model.
D. Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.

Answer: D

NEW QUESTION # 259
You are building a RAG system in IBM watsonx for a legal document retrieval platform. The platform deals with highly structured legal texts and unstructured client queries. Your task is to ensure that both structured documents and unstructured queries can be efficiently retrieved using vector embeddings generated by the system.
Which of the following actions will help you optimize the generation of vector embeddings to support accurate retrieval in the RAG system, considering the diverse data types? (Select two)

A. Use a separate embedding model for legal documents and client queries to account for differences in data structure.
B. Rely on pre-trained general-purpose embeddings without any domain-specific adaptation, as they provide broad coverage across many industries.
C. Generate embeddings for legal documents at the sentence level rather than the document level, as this increases precision in retrieval.
D. Ensure that embeddings for legal documents are based solely on the document metadata, as it contains the most important information.
E. Fine-tune the embedding model on legal text data to ensure embeddings capture the specific structure and semantics of the documents.

Answer: A,E

NEW QUESTION # 260
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