최신 C1000-185 무료덤프 - IBM watsonx Generative AI Engineer - Associate

A. Tools to iteratively optimize the model's alignment with human preferences, such as reinforcement learning from human feedback (RLHF)

B. A fine-tuning mechanism based on few-shot learning that only updates the model's output layer

C. Prompt engineering module designed to automatically generate synthetic training data for prompt-tuned models

D. A tokenization algorithm designed to reduce model size by removing unused tokens

A. Set top-k to 50

B. Set top-k to 5

C. Set top-k to 1

D. Set top-k to 1000

A. Providing a list of credible sources in the prompt

B. Implementing zero-shot learning techniques

C. Increasing the model's temperature parameter

D. Using more open-ended prompts

A. Ensure that all features are included in the model to capture as much data context as possible, regardless of their relevance.

B. Select only high-dimensional features to increase the complexity of the model and boost its predictive power.

C. Aggregate similar data types to minimize the need for feature selection during model optimization.

D. Use feature selection techniques to reduce dimensionality, enhancing model efficiency without sacrificing performance.

A. Provide the customer's emotional context for better alignment with the tone

B. Include examples of informal customer service responses for variability

C. Ask the model to generate multiple versions of the response and rank them

D. Specify the output tone as polite and professional

E. Instruct the model to limit responses to a specific character count

A. Using temperature control during generation to manage diversity in responses

B. Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases

C. Incorporating fairness constraints during the model's training phase

D. Training the model on a diverse and representative dataset

A. Ensure proper tokenization of the dataset according to the model's vocabulary

B. Randomly shuffle the dataset before training to increase diversity

C. Remove rare entities to improve model performance on common entities

D. Use sentence segmentation to isolate each named entity in its own sentence

A. Focus exclusively on endpoint authentication without enforcing any rate limits to prevent user frustration.

B. Implement strict IP whitelisting and authentication, but allow unfiltered prompts for flexibility.

C. Allow all external applications unrestricted access to the endpoint to facilitate wider model usage.

D. Limit the number of API calls per minute, restrict prompt lengths, and apply input validation to prevent malicious queries.

A. Fine-tuning the model exclusively on binary classification tasks to improve its generalization on all other tasks

B. Repeated fine-tuning of a model using reinforcement learning, focusing on aligning its outputs with human preferences across a diverse set of tasks

C. A single training run of the model on a dataset to generate better predictions for a fixed number of prompts

D. Direct training of the model on an expanded version of the dataset, without adjusting prompts or training tasks

A. Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.

B. Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.

C. Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.

D. Apply model quantization to optimize the LLM for handling long legal documents more efficiently.

A. Tuning Studio offers fine-tuning with minimal data, allowing users to adapt models to niche tasks without needing large datasets.

B. Tuning Studio enables real-time, dynamic adjustments to the model's architecture during inference to handle new tasks.

C. Tuning Studio allows selective parameter updates, reducing the need to retrain the entire model for each new task.

D. Tuning Studio integrates with existing AI infrastructure to streamline model fine-tuning without requiring complex deployment processes.

A. Fine-tune the model on domain-specific data to improve factual accuracy and relevance.

B. Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.

C. Lower the model's perplexity score to improve both completeness and factual accuracy.

D. Increase the length of the input prompt to ensure that responses are more complete.

A. Implement LangChain to handle complex multi-step workflows, using watsonx.ai's APIs to generate responses at specific stages in the chain.

B. Directly integrate the external APIs with watsonx.ai without any intermediate framework, since LangChain would add unnecessary overhead.

C. Write custom scripts to manage all prompt sequences manually, leveraging watsonx.ai's SDK to call the generative AI model at every step.

D. Use only watsonx.ai's built-in APIs and SDKs for integration, as LangChain is not required for chaining multiple prompts.

A. Manually crafting complex datasets by sampling individual instances from unrelated domains

B. Using the watsonx UI to generate synthetic data that mirrors your existing dataset, filling any data gaps

C. Relying exclusively on pre-trained general models without making domain-specific modifications

D. Using unsupervised learning on your existing dataset without adding synthetic data

A. Focus on generating prompts specific to a single domain to train the model on specialized tasks.

B. Prioritize prompts with repetitive patterns to help the model memorize key responses.

C. Generate a single highly-detailed prompt that covers all potential use cases to maximize generalization.

D. Use a diverse set of prompts covering multiple task domains with varying levels of complexity.

A. It ensures that the code snippets returned are exact matches to the keywords in the query, avoiding irrelevant code.

B. It retrieves code snippets based on the semantic similarity between the query and the code, enabling the discovery of relevant code even when the query and code do not use the same keywords.

C. It provides real-time updates to the code embeddings, ensuring the latest code is always retrieved.

D. It speeds up retrieval by limiting the search to repositories where the user has committed code in the past.

A. It allows the fine-tuning of the model's hyperparameters based on the specific domain, improving relevance and reducing token generation costs.

B. It provides tools for manual annotation of data to improve model accuracy.

C. It automatically generates custom datasets for training without needing labeled data.

D. It increases the model's maximum token limit, allowing for more extensive outputs without sacrificing performance.

A. LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.

B. LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.

C. LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.

D. LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.

A. Increase the number of epochs

B. Enable early stopping

C. Decrease the batch size

D. Use a larger validation set