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C1000-185 Demo Test | Reliable C1000-185 Braindumps Pdf

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q307-Q312):

NEW QUESTION # 307
You are implementing a Retrieval-Augmented Generation (RAG) system using LangChain, IBM WatsonX, and a vector database. The system needs to answer complex technical questions by retrieving relevant technical documents and generating a coherent response.
Which of the following best describes LangChain's role in the RAG pattern implementation?

A. LangChain optimizes document retrieval by pre-generating responses based on common queries and caching them in memory for future use.
B. LangChain allows WatsonX's LLM to fine-tune its parameters based on user interactions to improve future retrieval and generation accuracy.
C. LangChain is responsible for storing and retrieving documents using a sparse keyword-based retriever from a traditional relational database.
D. LangChain serves as an intermediary, facilitating communication between the vector database and WatsonX's LLM, ensuring that retrieved documents are contextually relevant for the LLM's response generation.

Answer: D

NEW QUESTION # 308
You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A. Ensure that the synthetic data covers edge cases as well as common industry scenarios.
B. Generate only a small amount of synthetic data to minimize computational costs and training time.
C. Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
D. Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
E. Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.

Answer: A,E

NEW QUESTION # 309
A healthcare organization is deploying a generative AI model to assist in summarizing patient records. Given the sensitivity of patient data, the model must not output Personally Identifiable Information (PII) such as names, addresses, or Social Security numbers.
Which of the following strategies would best help prevent the model from unintentionally generating PII?

A. Disable the model's ability to use context from previous tokens to prevent PII from being included in the output.
B. Use a post-processing step to detect and mask PII after the model generates the text.
C. Increase the temperature and top-k values to reduce the probability of PII being generated.
D. Fine-tune the model specifically on anonymized datasets with PII removed.

Answer: D

NEW QUESTION # 310
In a scenario where a developer is creating reusable prompt templates for a Watsonx Generative AI project, what is the most effective method to track the usage and performance of these templates over time?

A. Relying on manual tracking of templates in a spreadsheet for each generation
B. Leveraging Watsonx's native analytics and monitoring tools with built-in prompt tracking features
C. Embedding unique identifiers in the prompt templates and using Watsonx's logging mechanisms
D. Using static prompt templates without any tracking, as tracking adds unnecessary overhead

Answer: C

NEW QUESTION # 311
When optimizing the tuning process in IBM Watsonx Tuning Studio for a Generative AI model, which approach would best reduce training time and computational cost while maintaining model performance?

A. Perform full-scale retraining of the model for each new task to ensure maximum adaptability and accuracy.
B. Use all available training data, including unrelated examples, to ensure the model has a broad understanding of multiple tasks before tuning.
C. Increase the batch size and reduce the learning rate simultaneously to speed up the tuning process and minimize training iterations.
D. Focus the tuning on adjusting only the model's last few layers, which are responsible for task-specific outputs, while leaving the majority of the model unchanged.

Answer: D

NEW QUESTION # 312
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