Free PDF Quiz 2026 Reliable IBM C1000-185: Real IBM watsonx Generative AI Engineer - Associate Exam Questions

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q79-Q84):

NEW QUESTION # 79
A generative AI model is given the following prompt: "Translate the following sentence into French: 'The sun is shining brightly today.'" No additional context or examples are provided.
This is an example of which type of prompting and why is it likely to succeed?

  • A. Zero-shot prompting, but it will likely fail since translation tasks always need examples for accuracy.
  • B. Few-shot prompting, because the task requires translation examples to guide the model.
  • C. Zero-shot prompting, because the model is expected to perform the task without any example.
  • D. Zero-shot prompting, but it will only succeed if the prompt includes a few additional translation examples.

Answer: C


NEW QUESTION # 80
You are tasked with designing a prompt for a sentiment analysis model based on a large language model (LLM). The goal is to generate a coherent response from the model that aligns with a particular sentiment (positive, negative, or neutral) for customer reviews of a product.
Which of the following prompt designs are best suited to generate a positive review response? (Select two)

  • A. "Analyze the product based on the customer feedback and write a review that covers all sentiments."
  • B. "Describe the product as if you were a very satisfied customer, and you were recommending it to a friend."
  • C. "Write a review about the product that highlights both its pros and cons."
  • D. "Write a neutral review, neither praising nor criticizing the product."
  • E. "Generate a positive review about the product, focusing on the key strengths and avoiding any negative aspects."

Answer: B,E


NEW QUESTION # 81
You are tasked with fine-tuning a large language model (LLM) to perform sentiment analysis on product reviews. The dataset contains customer reviews, but some reviews are very short, and others contain irrelevant data like product specifications or spam. You want to prepare the dataset for fine-tuning by ensuring the data is clean, relevant, and representative of the task at hand.
Which of the following steps is most critical to ensure the dataset is suitable for fine-tuning?

  • A. Increase the number of epochs to handle the noisy data
  • B. Increase the learning rate to adjust for inconsistencies in the dataset
  • C. Randomly remove 10% of the dataset to reduce noise
  • D. Filter the dataset to remove irrelevant or outlier reviews

Answer: D


NEW QUESTION # 82
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 is highly effective when multiple equally probable tokens are available at each step
  • B. Greedy decoding works best when combined with temperature scaling to increase randomness
  • C. Greedy decoding guarantees the highest overall probability for the output sequence
  • D. Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence

Answer: D


NEW QUESTION # 83
You are working on a task that involves generating marketing copy using IBM Watsonx. The goal is to craft a prompt that leads to detailed and persuasive content about a new product launch.
Which of the following approaches would most likely result in high-quality, detailed, and contextually appropriate content?

  • A. Avoid specifying any constraints and rely on Watsonx's default model behavior: "Write product launch marketing content."
  • B. Provide specific context and audience information: "Generate marketing copy for a new eco-friendly water bottle targeting health-conscious consumers. Include persuasive language and focus on the sustainability features of the product."
  • C. Use a very short prompt: "Generate marketing copy for a product launch."
  • D. Use complex, technical jargon to generate highly specific content: "Produce syntactically dense prose with multifaceted aspects of ecological ramifications and commodification for a consumer base."

Answer: B


NEW QUESTION # 84
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