TIPS TO CRACK THE IBM C1000-185 EXAM

Tips to Crack the IBM C1000-185 Exam

Tips to Crack the IBM C1000-185 Exam

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q13-Q18):

NEW QUESTION # 13
You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?

  • A. Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.
  • B. Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.
  • C. Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.
  • D. Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.

Answer: B


NEW QUESTION # 14
You are fine-tuning a large language model (LLM) with InstructLab to generate high-quality summaries for long-form text documents.
After conducting an initial experiment, the performance seems suboptimal, particularly on technical documents with specialized vocabulary.
What steps could you take to improve the fine-tuning process to achieve better performance? (Select two)

  • A. Use greedy decoding during inference to generate concise summaries.
  • B. Add more domain-specific training data to improve the model's understanding of technical vocabulary.
  • C. Perform prompt engineering by providing specific task instructions for summarization.
  • D. Experiment with a smaller model to reduce complexity and increase inference speed.
  • E. Increase the learning rate for better adaptation to specialized data.

Answer: B,C


NEW QUESTION # 15
After completing a prompt-tuning experiment, you notice that the model's accuracy in generating relevant responses is high, but the fluency and grammatical correctness of the outputs seem to be suboptimal.
What statistical metric would most directly indicate this issue, and what action should you take to improve the output?

  • A. F1 score; increase the training dataset size to improve overall accuracy.
  • B. Perplexity score; apply additional language model fine-tuning on grammatical correctness.
  • C. BLEU score; improve prompt engineering to ensure that the model focuses on fluency.
  • D. ROUGE score; adjust the token generation limit to ensure longer outputs.

Answer: B


NEW QUESTION # 16
You have just finished a prompt tuning experiment for a large language model (LLM) to optimize its output for generating customer support summaries. The tuning results show that while the accuracy of the generated summaries is high (95%), the response time for generating them has significantly increased. The experiment data suggests that increasing the maximum token length during tuning led to better quality summaries but with slower generation.
Which parameter should you adjust to improve the model's response time without sacrificing too much summary quality?

  • A. Decrease the number of epochs
  • B. Increase the batch size
  • C. Decrease the maximum token length
  • D. Change the decoding strategy from beam search to greedy decoding

Answer: D


NEW QUESTION # 17
You are designing a generative AI model to generate detailed technical

  • A. Max Tokens = 500: This provides enough space for a detailed technical
  • B. Stop Sequence = 'END', Max Tokens = 500
  • C. Stop Sequence = None, Max Tokens = 700
  • D. Stop Sequence = '<END>', Max Tokens = 100

Answer: B


NEW QUESTION # 18
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