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Training C1000-185 Pdf & Exam C1000-185 Demo

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q203-Q208):

NEW QUESTION # 203
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. Linear search over the entire vector dataset.
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. Exact nearest neighbor (ENN) search using KD-trees.

Answer: B

NEW QUESTION # 204
You are tasked with generating synthetic data for fine-tuning a large language model (LLM) using the IBM Watsonx User Interface. You want to generate relevant training samples to improve the model's accuracy in a text classification task.
Which action should you prioritize to generate high-quality synthetic data using the interface?

A. Avoid using domain-specific prompts to keep the synthetic data unbiased and more generalizable.
B. Select only a few generic prompts to generate the largest volume of data possible, ensuring variety.
C. Choose a diverse set of prompts that span different domains unrelated to the original dataset.
D. Customize prompt templates to closely mimic the structure and format of the original training data.

Answer: D

NEW QUESTION # 205
You are tasked with integrating third-party embedding models into a Retrieval-Augmented Generation (RAG) system for document retrieval. Several models offer pre-trained embeddings that can be leveraged for a variety of downstream tasks.
Which of the following third-party models is designed for generating embeddings that capture semantic meaning and context, making it ideal for a RAG-based GenAI system?

A. Naive Bayes Classifier
B. BERT
C. GPT-3 Embeddings
D. WordNet

Answer: B

NEW QUESTION # 206
Which of the following is the most effective approach when planning for data elements to optimize application usage in IBM watsonx generative AI models?

A. Use feature selection techniques to reduce dimensionality, enhancing model efficiency without sacrificing performance.
B. Ensure that all features are included in the model to capture as much data context as possible, regardless of their relevance.
C. Aggregate similar data types to minimize the need for feature selection during model optimization.
D. Select only high-dimensional features to increase the complexity of the model and boost its predictive power.

Answer: A

NEW QUESTION # 207
A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back-end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer's intent.
Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?

A. Set up IBM watsonx to queue customer interactions and process them sequentially using a single system at a time.
B. Use synchronous REST APIs for all back-end systems, relying on watsonx's built-in intent recognition to manage API routing.
C. Implement a monolithic architecture where all system interactions are handled within a single service.
D. Utilize IBM watsonx's API orchestration layer to dynamically route requests to the correct system based on intent analysis.

Answer: D

NEW QUESTION # 208
......

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Ben Brown Ben Brown

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