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NEW QUESTION # 287
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?
Answer: D
Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case. Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
* Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
* Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
* Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
* Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
References:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com
/bedrock/latest/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock
/latest/devguide/)
NEW QUESTION # 288
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.
Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?
Answer: C
NEW QUESTION # 289
A manufacturing company has an application that ingests consumer complaints from publicly available sources. The application uses complex hard-coded logic to process the complaints. The company wants to scale this logic across markets and product lines.
Which advantage do generative AI models offer for this scenario?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Generative AI models offer adaptability, meaning they can generalize across:
* Different markets
* New product lines
* Variations in language and complaint structure
Unlike hard-coded logic, generative models can adapt to new patterns and inputs without requiring extensive rule rewrites, making them ideal for scaling text-based processing applications.
Why the other options are incorrect:
* Predictability (A) is typically lower in generative models.
* Less sensitivity (C) is incorrect; generative models are sensitive to input variations.
* Explainability (D) is generally limited in large generative models.
AWS AI document references:
* Generative AI Benefits and Trade-offs
* Modernizing Text Processing with Foundation Models
* Scaling NLP Solutions on AWS
NEW QUESTION # 290
A financial company is training a generative AI model to predict outcomes of loan applications. The training dataset is small. The dataset categorizes loan applicants as " younger-aged, " " middle-aged, " or " older-aged.
" Most individuals in the dataset are characterized as " middle-aged. " The company removes the age range feature from the training dataset.
Which model behavior will likely happen as a result of this change to the dataset?
Answer: C
NEW QUESTION # 291
An AI practitioner has trained a model on a training dataset. The model performs well on the training data. However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?
Answer: D
Explanation:
When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.
* Overfitting: The model memorizes the training data patterns instead of generalizing.
* Underfitting (A) means the model performs poorly on both training and test data.
* Bias (C) refers to systemic errors in predictions, not this training/test mismatch.
* Prompt engineering (B) applies to generative AI, not general ML training models.
# Reference:
AWS ML Glossary - Overfitting and Underfitting
NEW QUESTION # 292
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