PASS GUARANTEED QUIZ IBM - TRUSTABLE C1000-185 - IBM WATSONX GENERATIVE AI ENGINEER - ASSOCIATE SAMPLE EXAM

Pass Guaranteed Quiz IBM - Trustable C1000-185 - IBM watsonx Generative AI Engineer - Associate Sample Exam

Pass Guaranteed Quiz IBM - Trustable C1000-185 - IBM watsonx Generative AI Engineer - Associate Sample Exam

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Tags: C1000-185 Sample Exam, C1000-185 Questions, C1000-185 Latest Test Practice, C1000-185 Pdf Format, C1000-185 Test Braindumps

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q25-Q30):

NEW QUESTION # 25
In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

  • A. Reduce the model's token limit to restrict the amount of text generated
  • B. Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])
  • C. Introduce explicit instructions in the prompt to avoid generating personal information
  • D. Increase the model's randomness by adjusting the temperature parameter

Answer: C


NEW QUESTION # 26
You're designing a prompt that should generate text for product descriptions using a generative AI model. You want to limit the model's word choices to ensure coherence while still allowing some flexibility for creativity. You decide to use Top-P (nucleus) sampling to achieve this.
Which of the following settings for the Top-P parameter is most appropriate to strike a balance between creativity and coherence?

  • A. Top-P = 1.0
  • B. Top-P = 0.1
  • C. Top-P = 0.9
  • D. Top-P = 0.3

Answer: C


NEW QUESTION # 27
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. Focus only on neutral and negative examples to challenge the model
  • B. Convert neutral examples into either positive or negative to simplify the task
  • 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 # 28
In what situation might greedy decoding fail to generate an optimal output, even though it consistently chooses the most probable token at each step?

  • A. Greedy decoding works best when combined with temperature scaling to increase randomness
  • B. Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
  • C. Greedy decoding guarantees the highest overall probability for the output sequence
  • D. Greedy decoding is highly effective when multiple equally probable tokens are available at each step

Answer: B


NEW QUESTION # 29
In the context of Retrieval-Augmented Generation (RAG) models, embeddings play a crucial role in retrieving relevant documents.
Which of the following best describes the purpose of embeddings in GenAI, particularly within a RAG system?

  • A. Embeddings are dense vector representations of words, phrases, or entire documents that capture semantic relationships, making it easier to find similar content in the knowledge base during retrieval.
  • B. Embeddings represent each word in a fixed-length vector based on its semantic meaning, which allows for precise retrieval of documents by directly matching words between the query and the documents.
  • C. Embeddings are primarily used to measure the grammatical correctness of the generated responses by checking the sentence structure in the generated text.
  • D. Embeddings are used to compress large documents into a smaller format, enabling faster storage and retrieval within a RAG system.

Answer: A


NEW QUESTION # 30
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