최신IBM watsonx Generative AI Engineer - Associate - C1000-185무료샘플문제

A. They allow for faster deployment of new AI-driven solutions since the same prompt templates can be adapted for multiple tasks with minimal changes.

B. Reusable prompts ensure that the AI model continues to evolve without additional training, as the prompts themselves become more refined over time.

C. Reusable prompts automatically adjust their structure depending on the input, eliminating the need for dynamic parameters or customization.

D. Reusable prompts eliminate the need for monitoring model performance since they consistently deliver accurate results.

A. Use Watson Discovery for summarizing documents and watsonx.ai's LLM for only retrieving relevant content from the documents.

B. Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai's LLM for summarization through API calls.

C. Use watsonx.ai's LLM to both retrieve and summarize the documents, bypassing Watson Discovery.

D. Use watsonx.ai's LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.

A. Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.

B. Tuning Studio allows for the customization of training data at runtime without needing pre-processing.

C. Tuning Studio automatically generates prompt templates that can be used for different tasks without further configuration.

D. Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.

A. When you need to provide answers based on the keywords present in the documents, as vector databases are designed for keyword-based retrieval.

B. When storing highly structured relational data, as a vector database excels at managing tabular information efficiently.

C. When working with large amounts of unstructured text data, to enable semantic search through embeddings that represent the meaning of documents.

D. When performing traditional database operations like sorting and filtering based on numeric or categorical values.

A. Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.

B. Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.

C. Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.

D. Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.

A. Rely on pre-trained general-purpose embeddings without any domain-specific adaptation, as they provide broad coverage across many industries.

B. Fine-tune the embedding model on legal text data to ensure embeddings capture the specific structure and semantics of the documents.

C. Ensure that embeddings for legal documents are based solely on the document metadata, as it contains the most important information.

D. Use a separate embedding model for legal documents and client queries to account for differences in data structure.

E. Generate embeddings for legal documents at the sentence level rather than the document level, as this increases precision in retrieval.

A. Use beam search decoding with a low beam width and a high repetition penalty.

B. Increase the temperature to 1.5 and set a high maximum token limit.

C. Set a low top-k value and implement a repetition penalty with a low maximum token limit.

D. Use greedy decoding with no repetition penalty and a high stopping probability threshold.

A. Soft prompts provide more direct control over the model's behavior by offering explicit, human-readable instructions.

B. Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.

C. Soft prompts allow for more nuanced control of the model's behavior through learned embeddings, which can adapt to a variety of tasks without requiring explicit human intervention.

D. Soft prompts enforce stricter generation outputs due to the deterministic nature of the learned embeddings, leading to higher consistency in results.

A. Keras for handling sparse retrieval and pre-trained generation models.

B. Hugging Face Transformers for language generation and FAISS for embedding-based document retrieval.

C. PyTorch for implementing rule-based retrieval and generative model fine-tuning.

D. TensorFlow for dense retrieval and document embedding generation.

A. Loading the entire model into memory at runtime to avoid latency issues

B. Monitoring and adjusting resource allocation dynamically based on usage statistics

C. Employing model quantization techniques during deployment

D. Using batch processing instead of real-time inference for all requests

E. Relying on a single model configuration across all hardware types to simplify deployment