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NEW QUESTION # 135
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)
Answer: A,E
Explanation:
I'll continue with more questions. Stay tuned!
NEW QUESTION # 136
An AI practitioner has trained a model on a training dataset. The model performs well on the training dat a. However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?
Answer: C
Explanation:
Comprehensive and Detailed
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 # 137
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: D
Explanation:
Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM's performance against known standards with minimal administrative effort.
* Option D (Correct): "Benchmark datasets": This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.
* Option A: "User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.
* Option B: "Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.
* Option C: "Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.
AWS AI Practitioner References:
* Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.
NEW QUESTION # 138
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?
Answer: C
Explanation:
The correct answer is B because Amazon SageMaker JumpStart provides pre-built solutions, including training workflows for popular open-source LLMs such as Falcon, LLaMA, and others. It allows practitioners to quickly launch fine-tuning jobs using predefined templates, minimizing operational setup and code complexity.
From AWS documentation:
"Amazon SageMaker JumpStart enables you to fine-tune and deploy foundation models with minimal setup.
It provides easy-to-use interfaces and pre-built configurations for training, which significantly reduces the operational overhead required to train models." Explanation of other options:
A). PartyRock is designed for prototyping generative AI apps but does not support model training or fine- tuning.
C). Writing a custom script for SageMaker training is flexible but involves more operational effort, including handling infrastructure configuration.
D). Training on EC2 via a Jupyter notebook is fully manual and operationally intensive, including dependency setup, data handling, and resource scaling.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon SageMaker JumpStart Developer Guide - Fine-tuning Foundation Models
* AWS Certified Machine Learning Specialty Guide - Model Customization and JumpStart
NEW QUESTION # 139
Which option is a use case for generative AI models?
Answer: B
Explanation:
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner Reference:
Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
NEW QUESTION # 140
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