C1000-185 EXAM EXERCISE | C1000-185 REGUALER UPDATE

C1000-185 Exam Exercise | C1000-185 Regualer Update

C1000-185 Exam Exercise | C1000-185 Regualer Update

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Tags: C1000-185 Exam Exercise, C1000-185 Regualer Update, C1000-185 Test Result, New C1000-185 Test Pass4sure, C1000-185 Reliable Test Materials

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q217-Q222):

NEW QUESTION # 217
You are using IBM Watsonx to control the randomness of a language model's output by adjusting the top-k parameter.
What happens when you reduce the top-k value from 50 to 5 during text generation?

  • A. Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.
  • B. Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.
  • C. Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.
  • D. The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.

Answer: D


NEW QUESTION # 218
In a Retrieval-Augmented Generation (RAG) system designed for technical document retrieval, you are tasked with implementing text chunking techniques using the LangChain library. The technical documents are large and contain numerous tables, figures, and bullet points.
What is the most effective way to handle text splitting to ensure high-quality retrieval?

  • A. Convert tables and figures into plain text and split the document by character count to maintain even chunk sizes.
  • B. Use a hybrid approach, splitting the text by both semantic boundaries (like paragraphs) and content-specific markers (like bullet points and tables), while keeping chunks within the model's token limit.
  • C. Split the text into equal-sized chunks of 512 characters, regardless of the content structure, to improve consistency in retrieval.
  • D. Split the text only at paragraph breaks, ignoring tables and figures, as they can be processed separately.

Answer: B


NEW QUESTION # 219
You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

  • A. Implement a post-processing step that filters for factual accuracy using a knowledge base.
  • B. Use beam search with a high beam width to generate the most likely sequence.
  • C. Reduce the maximum token limit and set a higher repetition penalty.
  • D. Increase the model's temperature to allow for more diverse outputs.

Answer: A


NEW QUESTION # 220
You are tasked with deploying a watsonx Generative AI solution for a client who requires real-time inference with minimal latency. The solution will be used for large-scale text generation tasks, where throughput is critical. The client also requires the system to handle fluctuating loads efficiently.
What would be the most appropriate deployment strategy?

  • A. Deploy the model on a multi-node GPU cluster with Kubernetes to handle auto-scaling and fault tolerance.
  • B. Use a CPU-based deployment for cost efficiency, and manually adjust the number of instances as needed.
  • C. Deploy the model on a single GPU-based cloud instance with auto-scaling enabled.
  • D. Deploy the model in a hybrid architecture, splitting inference tasks between on-premise servers and cloud resources.

Answer: A


NEW QUESTION # 221
Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

  • A. Fine-tune the model using only the training data from trusted sources, without expanding the dataset.
  • B. Use adversarial training techniques to make the model more robust against bias and poisoning.
  • C. Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.
  • D. Apply prompt engineering techniques to avoid triggering known biases in the model.

Answer: C


NEW QUESTION # 222
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