ONE OF THE BEST WAYS TO PREPARE FOR THE C1000-185

One of the Best Ways to Prepare For the C1000-185

One of the Best Ways to Prepare For the C1000-185

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Tags: C1000-185 Training Online, C1000-185 Exam Experience, Free C1000-185 Exam Questions, C1000-185 Reliable Test Pattern, Exam C1000-185 Fees

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q194-Q199):

NEW QUESTION # 194
You have been using a pre-trained foundation model for a financial text summarization application. While the model is generating summaries that are generally accurate, it sometimes fails to handle domain-specific financial jargon. You are considering whether it's time to tune the model to optimize its performance for this task.
Which of the following conditions would most strongly justify tuning the foundation model for your specific use case?

  • A. The model's accuracy fluctuates based on the length of the input text.
  • B. The model produces a high number of tokens in each output, which increases the cost of usage.
  • C. The model exhibits acceptable performance but occasionally generates off-topic responses unrelated to financial data.
  • D. The model generates output that is highly relevant to general topics but often misinterprets industry-specific terms like "leverage" or "derivative."

Answer: D


NEW QUESTION # 195
In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)

  • A. "Recommend products"
  • B. "[product_category]"
  • C. "[customer_name]"
  • D. "for"
  • E. "based on their interest"

Answer: B,C


NEW QUESTION # 196
You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?

  • A. Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
  • B. Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
  • C. Fine-tuning the generative model on the entire document corpus without retrieval components.
  • D. Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.

Answer: B


NEW QUESTION # 197
You have successfully deployed a custom model using IBM Watson Machine Learning. Now, an application needs to access the model for inference.
Which of the following configurations will ensure that the application has appropriate access to the deployed model?

  • A. Create a public-facing API endpoint without authentication
  • B. Implement OAuth2 authentication using IBM Cloud IAM for securing the API.
  • C. Assign the "viewer" role to the application's service account in Watson Machine Learning
  • D. Use the default IAM policies for all users in the IBM Cloud project

Answer: B


NEW QUESTION # 198
In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

  • A. Post-training quantization is more resource-efficient than quantization-aware training, as it applies quantization after the model has been fully trained, eliminating the need for additional fine-tuning.
  • B. Quantization can increase the inference time of a model since it adds computational complexity when converting from higher to lower precision formats during runtime.
  • C. Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.
  • D. Quantization reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
  • E. Quantizing a model to 8-bit precision always results in a significant loss in performance, especially when working with language models or large generative AI architectures.

Answer: C,D


NEW QUESTION # 199
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