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C1000-185 New Braindumps Files & Exam C1000-185 Quiz

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q352-Q357):

NEW QUESTION # 352
While working with IBM Watsonx, you are asked to ensure that the synthetic data generated has a low privacy leakage probability. During the generation process, you must adjust settings related to privacy leakage probability.
Which factor most directly affects the probability of privacy leakage when generating synthetic data with differential privacy?

A. The amount of noise added to the synthetic data during the differential privacy process.
B. The size of the original dataset used for training.
C. The use of ensemble models to train on different portions of the dataset and reduce dependency on individual records.
D. The quality and variance of the synthetic data generated.

Answer: A

NEW QUESTION # 353
In the IBM watsonx Prompt Lab, you are tasked with improving a customer-facing chat model by editing and refining prompts.
Which of the following is an effective prompt editing option to fine-tune a chat-based generative AI model?

A. Altering the tone of the prompt to make it more aligned with the brand's voice and customer expectations.
B. Modifying the stop sequence to ensure the model responds with shorter answers for concise customer interactions.
C. Increasing the temperature parameter to make the model more deterministic and consistent in its responses.
D. Decreasing the number of tokens allowed in the output to encourage more creative responses.

Answer: A

NEW QUESTION # 354
You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?

A. Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.
B. Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.
C. Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.
D. Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.

Answer: D

NEW QUESTION # 355
You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?

A. Proceed with training the model without any changes, as missing data will not impact NLP models significantly.
B. Delete all rows containing null values in the 'ticket_description' field.
C. Replace all null values with zeros, as this will standardize the dataset.
D. Use Data Refinery's "Data Quality" feature to generate a profiling report and assess the scope of the issues.

Answer: D

NEW QUESTION # 356
You are analyzing prompts submitted to a Generative AI model used for summarizing long research papers.
One user submits the following prompt: "Summarize this 40-page research paper on quantum computing, including details on every section and subsection, providing a detailed description of key points, methodologies, results, discussions, and future work. The summary should be at least 5 pages long." Why is this prompt considered inefficient, and how should it be optimized?

A. The prompt is inefficient because it asks for too much detail across all sections, leading to excessive token usage and unnecessary information in the output.
B. The prompt is efficient because it clearly outlines the expectations and ensures a comprehensive summary.
C. The prompt is inefficient because it does not specify a character limit, which means the model might generate overly verbose output.
D. The prompt is inefficient because it requests a 5-page summary, which is unnecessary for summarizing the key information from a research paper.

Answer: A

NEW QUESTION # 357
......

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