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NEW QUESTION # 69
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: C,E
Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
* A. Include Fairness Metrics for Model Evaluation:
* Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
* These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
* C. Modify the Training Data to Mitigate Bias:
* Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
* Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
* Why Other Options are Incorrect:
* B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
* D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
* E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.
NEW QUESTION # 70
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency
Answer:
Explanation:
Explanation:
AWS References:
Amazon Bedrock - Model parameters and context window
AWS ML Inference - Latency and Throughput
AWS Scalability - Concurrency
NEW QUESTION # 71
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: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Removing a relevant feature from a small and imbalanced dataset can reduce the model's ability to:
* Learn meaningful distinctions between groups
* Generalize well for underrepresented categories
AWS Responsible AI guidance explains that insufficient or removed features can lead to reduced predictive performance, especially for minority groups.
Why the other options are incorrect:
* B is unrelated to model behavior.
* C is unsupported by the scenario.
* D is unlikely given data imbalance and feature removal.
AWS AI document references:
* Bias and Data Representation in ML
* Responsible Feature Selection
* Dataset Imbalance and Model Performance
NEW QUESTION # 72
A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?
Answer: C
Explanation:
The correct answer is C because Amazon Nova Reel is the AWS foundation model designed for generative video use cases, providing end-to-end video generation using generative AI, which significantly reduces video creation time and eliminates the need for manual assembly.
According to AWS Bedrock documentation:
" Amazon Nova Reel enables users to generate short-form video content directly from prompts, including the ability to define style, motion, scenes, and transitions - streamlining the generative content creation process.
"
This is the most operationally efficient choice as it does not require stitching together images or using external editing tools.
Explanation of other options:
A and B involve generating intermediate images and then manually creating videos using video editing tools
- not operationally efficient.
D). Amazon Nova Pro is intended for high-end professional-grade image or 3D content generation, but not specifically optimized for video generation like Nova Reel.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Model Directory - Nova Models Overview
AWS GenAI Foundation Model Comparison Guide
AWS Generative AI for Creators Whitepaper (2024)
NEW QUESTION # 73
Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?
Answer: C
Explanation:
Comprehensive and Detailed
Retrieval-Augmented Generation (RAG) integrates external knowledge sources (databases, vector stores, document repositories) with LLMs, enabling them to generate contextually accurate and up-to-date responses without retraining.
B is incorrect: RAG does not speed up training; it improves inference results.
C is incorrect: speech recognition is not an RAG use case.
D is incorrect: computer vision augmentation is unrelated to RAG.
Reference:
AWS Documentation - Knowledge Bases for RAG in Amazon Bedrock
NEW QUESTION # 74
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