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C1000-185 Dumps Torrent: IBM watsonx Generative AI Engineer - Associate & C1000-185 Exam Bootcamp

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q49-Q54):

NEW QUESTION # 49
In a Retrieval-Augmented Generation (RAG) setup, you notice that the model is generating responses that are not always relevant to the query, despite the knowledge base containing useful information.
What could be the most likely cause of this issue, and how should you address it?

A. The retrieval mechanism might be failing to fetch the most relevant documents from the knowledge base, so you should improve the search algorithm or use a better ranking system.
B. The problem likely lies with the input format, so changing all queries to a pre-structured format (like templates) will ensure the retrieval and generation stages perform optimally.
C. The knowledge base might contain outdated or irrelevant documents, so removing all non-recent documents would ensure the model generates more relevant responses.
D. The model is over-relying on the retrieval system and ignoring the language model's ability to generate coherent responses, so you should disable the retrieval component for general questions.

Answer: A

NEW QUESTION # 50
You are fine-tuning a machine learning model using IBM Watsonx with a dataset that includes sensitive information. You decide to enable differential privacy while generating synthetic data to ensure the privacy of individual records.
What key feature of differential privacy ensures that the synthetic data does not leak private information from the original dataset?

A. Adding controlled noise to the data, ensuring that no individual's data point is easily distinguishable from aggregate data.
B. Masking sensitive data fields before creating the synthetic data, ensuring no private information is directly used.
C. Limiting the number of data points generated to avoid overfitting the synthetic data.
D. Using clustering techniques to group similar data points, preventing individual-level data from being exposed.

Answer: A

NEW QUESTION # 51
You are working with a foundation model pre-trained on a large general-purpose dataset, and you plan to deploy it for a specialized task in healthcare-related text generation. However, before tuning the model, you want to assess whether tuning is necessary for your use case.
Which of the following is the best indicator that it is time to tune the foundation model for your task?

A. You are noticing that the model occasionally makes grammar mistakes in the generated text.
B. The model's inference time is longer than expected, and you need to reduce latency for real-time applications.
C. The model's accuracy is already above 90%, but you want to achieve 95% accuracy for your task.
D. The model performs well on general datasets but fails to capture specific domain-related terminology and context.

Answer: D

NEW QUESTION # 52
You are designing a question-answering system that can provide responses based on a vast corpus of legal documents. Your solution leverages the Retrieval-Augmented Generation (RAG) pattern using an IBM watsonx-based architecture. The goal is to ensure that the system retrieves highly relevant documents and uses the retrieved content to generate accurate responses. The team is considering various libraries to integrate dense retrieval and generation models into this architecture.
Which of the following libraries or frameworks should you integrate to effectively implement the RAG pattern in this scenario, considering the need for both document retrieval and natural language generation?

A. Keras for handling sparse retrieval and pre-trained generation models.
B. PyTorch for implementing rule-based retrieval and generative model fine-tuning.
C. TensorFlow for dense retrieval and document embedding generation.
D. Hugging Face Transformers for language generation and FAISS for embedding-based document retrieval.

Answer: D

NEW QUESTION # 53
You are tasked with deploying a suite of AI assets, including a pretrained generative language model and a set of custom-trained models. Your company operates in an environment where scalability and adaptability are key, as customer needs vary significantly across regions and sectors. You need to ensure that the deployment of these AI assets can meet varying demand, maintain performance, and allow for customization based on specific client needs.
Which deployment strategy best balances scalability, performance, and adaptability for this suite of AI assets?

A. Deploy all AI assets as a single monolithic model on a multi-GPU setup, allowing scalability across multiple customers without the need for further customization.
B. Fine-tune the pretrained model for each client individually, deploy as separate models for each use case, and store them locally for each region.
C. Containerize each AI asset separately, deploy them as microservices on a cloud platform, and use load balancing to manage varying demand across regions.
D. Deploy the AI assets on edge devices near the customer, reducing latency and ensuring consistent performance without relying on cloud infrastructure.

Answer: C

NEW QUESTION # 54
......

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