EXAM DUMPS C1000-185 PDF PASS CERTIFY| VALID C1000-185 AUTHORIZED PDF: IBM WATSONX GENERATIVE AI ENGINEER - ASSOCIATE

Exam Dumps C1000-185 Pdf Pass Certify| Valid C1000-185 Authorized Pdf: IBM watsonx Generative AI Engineer - Associate

Exam Dumps C1000-185 Pdf Pass Certify| Valid C1000-185 Authorized Pdf: IBM watsonx Generative AI Engineer - Associate

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q140-Q145):

NEW QUESTION # 140
You are designing a Retrieval-Augmented Generation (RAG) model within IBM watsonx to assist in generating responses for a customer service chatbot. The model needs to leverage a knowledge base (KB) of articles to enhance the accuracy of responses.
Which of the following correctly describes how the RAG model can be implemented to achieve this goal?

  • A. The RAG model retrieves relevant knowledge from the knowledge base using a search or retrieval mechanism before generating a response, which is then influenced by this retrieved information.
  • B. The RAG model first generates an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.
  • C. The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.
  • D. The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.

Answer: A


NEW QUESTION # 141
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. Fine-tune the embedding model on legal text data to ensure embeddings capture the specific structure and semantics of the documents.
  • C. Rely on pre-trained general-purpose embeddings without any domain-specific adaptation, as they provide broad coverage across many industries.
  • D. Ensure that embeddings for legal documents are based solely on the document metadata, as it contains the most important information.
  • E. Generate embeddings for legal documents at the sentence level rather than the document level, as this increases precision in retrieval.

Answer: A,B


NEW QUESTION # 142
You are working as a generative AI engineer and have developed a custom large language model (LLM) optimized for a specific use case. You are tasked with deploying this model on the IBM Watsonx platform.
Which of the following steps is most essential to ensure the successful deployment of your custom model, given that the model uses a third-party transformer architecture?

  • A. Set up auto-scaling in the IBM Watsonx environment to handle large numbers of simultaneous model inference requests.
  • B. Ensure that the model's training data is in a proprietary IBM format, as only Watsonx-specific formats are supported for custom model deployments.
  • C. Containerize the model using Docker or an equivalent containerization tool, ensuring that all required dependencies, such as transformers, tokenizers, and necessary packages, are included.
  • D. Modify the model to use IBM's proprietary transformer architecture, as third-party architectures are not supported by Watsonx.

Answer: C


NEW QUESTION # 143
You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?

  • A. Convert neutral examples into either positive or negative to simplify the task
  • B. Focus only on neutral and negative examples to challenge the model
  • C. Increase the number of positive sentiment examples in the dataset
  • D. Ensure an equal distribution of positive, negative, and neutral sentiment examples

Answer: D


NEW QUESTION # 144
You are deploying a new version of a generative AI model in IBM Watsonx, and you want to maintain the integrity of prompt versioning throughout the deployment lifecycle.
Which of the following methods is the most effective for ensuring that the correct prompt version is used with the corresponding model version in production?

  • A. Hard-code the prompt within the model deployment script to ensure that the correct prompt is always used
  • B. Use the latest available prompt version for every deployment, without specifying an exact version
  • C. Implement model registry tags that associate a specific prompt version with each model version during deployment.
  • D. Use semantic versioning for both the model and the associated prompts, and track them independently in separate systems.

Answer: C


NEW QUESTION # 145
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