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NEW QUESTION # 109
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
Answer:
Explanation:
Explanation:
This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.
Scenario 1:
Encrypt the application data, and isolate the application on a private network.
# Principle: Privacy and Security
From AWS documentation:
"Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance." Scenario 2:
Evaluate how different population groups will be impacted.
# Principle: Fairness
From AWS documentation:
"The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected." Scenario 3:
Test the application with unexpected data to ensure the application will work in unique situations.
# Principle: Robustness
From AWS documentation:
"Robustness refers to an AI system's ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases." Referenced AWS AI/ML Documents and Study Guides:
* AWS Responsible AI Practices Whitepaper - Core Principles of Responsible AI
* Amazon Bedrock Documentation - Responsible AI and Safety Controls
* AWS Certified Machine Learning Specialty Guide - AI Governance and Model Evaluation
NEW QUESTION # 110
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM).
The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?
Answer: D
Explanation:
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
# Reference:
AWS Documentation - SageMaker Ground Truth Plus
NEW QUESTION # 111
An AI practitioner is developing a new ML model. After training the model, the AI practitioner evaluates the accuracy of the model's predictions. The model's accuracy is low when the model uses both the training dataset and the test dataset.
Which scenario is the MOST likely cause of this problem?
Answer: B
Explanation:
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. AWS documentation explains that an underfit model performs poorly on both training and test datasets, which directly matches the scenario described.
In this case, the model shows low accuracy during training and evaluation, indicating that it has not learned sufficient relationships from the data. AWS identifies common causes of underfitting as insufficient model complexity, inadequate feature representation, overly aggressive regularization, or insufficient training time.
Underfitting is different from overfitting. Overfitting occurs when a model performs well on training data but poorly on test data, which is not the situation here. Hallucination applies to generative AI outputs, not supervised ML model accuracy. Cross-validation is a model evaluation technique, not a cause of poor performance.
AWS emphasizes the importance of diagnosing underfitting early in the model development lifecycle.
Remedies include increasing model complexity, adding relevant features, reducing regularization, or selecting a more expressive algorithm. These steps allow the model to better learn from the data and improve accuracy across both training and test sets.
AWS machine learning best practices clearly associate low performance on both datasets with underfitting, making this the most likely cause of the problem described.
NEW QUESTION # 112
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
Answer: D
Explanation:
Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.
* Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.
* Option B: "Model customization" is a feature, not a pricing model.
* Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.
* Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.
AWS AI Practitioner References:
* AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.
NEW QUESTION # 113
A company wants to generate synthetic data responses for multiple prompts from a large volume of dat a. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
Answer: A
NEW QUESTION # 114
......
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