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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q309-Q314):

NEW QUESTION # 309
Which of the following practices are best suited to optimize the performance of a deployed generative AI model in IBM watsonx under real-world traffic conditions? (Select two)

A. Relying on a single model configuration across all hardware types to simplify deployment
B. Using batch processing instead of real-time inference for all requests
C. Employing model quantization techniques during deployment
D. Monitoring and adjusting resource allocation dynamically based on usage statistics
E. Loading the entire model into memory at runtime to avoid latency issues

Answer: C,D

NEW QUESTION # 310
You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

A. The model can generate outputs that are too general, failing to meet the specific needs of the user.
B. The model might produce content that doesn't align with the cultural preferences of the user.
C. The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
D. The model can generate overly creative or non-factual responses, leading to brand reputation damage.

Answer: C

NEW QUESTION # 311
A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?

A. It provides a hierarchical structure that helps the model understand the relationships between different medical concepts.
B. It reduces the overall size of the dataset by filtering out irrelevant data.
C. It eliminates the need for data cleaning and preprocessing since the taxonomy provides the structure.
D. It ensures that the model only focuses on specific medical specialties by eliminating other categories.

Answer: A

NEW QUESTION # 312
You are working on enhancing the search functionality in a customer service chatbot by implementing the Retrieval-Augmented Generation (RAG) pattern. The chatbot needs to answer customer queries about various technical issues by retrieving relevant information from a knowledge base. Your team is discussing different ways to structure the RAG system and how to implement the pattern efficiently using existing tools.
Which of the following statements best describes the RAG pattern, and how it should be implemented in the context of this chatbot?

A. The RAG pattern combines dense retrieval with a vector store, where retrieved documents are directly presented as final answers.
B. The RAG pattern prioritizes generating answers based on the frequency of document appearances in the retrieval phase, improving precision.
C. The RAG pattern integrates sparse retrieval with a rule-based system for generating responses based on exact document matches.
D. The RAG pattern enhances a generative model by retrieving relevant documents, which are then used as context for generating a final response.

Answer: D

NEW QUESTION # 313
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. Set a maximum token limit of 500 and implement a fixed length stopping criterion.
B. Set the top-k value to 1 and use the default maximum token limit, relying on the model's internal stopping criteria.
C. Use a stop sequence based on legal clause delimiters and impose a minimum token limit of 50.
D. Use a beam search algorithm and impose a strict length restriction of 100 tokens.

Answer: C

NEW QUESTION # 314
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2025 Realistic Customized C1000-185 Lab Simulation - IBM watsonx Generative AI Engineer - Associate Pdf Demo Download

C1000-185 Pdf Demo Download & Exam C1000-185 Simulator Free

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q309-Q314):

100% Pass Quiz 2025 C1000-185: IBM watsonx Generative AI Engineer - Associate – Valid Customized Lab Simulation

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