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NEW QUESTION # 102
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution ' s decisions to be explainable.
Which factor relates to the explainability of the AI solution ' s decisions?
Answer: B
Explanation:
The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions.
Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like " black boxes. " Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
" Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes. " (Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify) Detailed Explanation:
Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.
Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.
Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.
Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.
References:
Amazon SageMaker Developer Guide: Explainability with SageMaker Clarify (https://docs.aws.amazon.com
/sagemaker/latest/dg/clarify-explainability.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Explainability AWS Documentation: Explainable AI (https://aws.amazon.com/machine-learning/responsible-ai/)
NEW QUESTION # 103
A company created an AI voice model that is based on a popular presenter. The company is using the model to create advertisements. However, the presenter did not consent to the use of his voice for the model. The presenter demands that the company stop the advertisements.
Which challenge of working with generative AI does this scenario demonstrate?
Answer: A
Explanation:
This scenario demonstrates intellectual property (IP) infringement, where an AI-generated artifact (a cloned voice) uses the identifiable traits of a person without permission. According to AWS Responsible AI practices, using a person's likeness, image, or voice in generative AI systems requires explicit rights and consent. Foundation models trained or applied without respecting these ownership boundaries violate ethical and legal frameworks under IP law. The AWS documentation emphasizes that generative AI must respect copyrighted or trademarked works and human likenesses. Responsible deployment requires consent-based data usage, license tracking, and content watermarking to prevent misappropriation. AWS Bedrock enforces such compliance principles through Guardrails and policy-based moderation to ensure content generation aligns with ethical and lawful standards.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Guidelines - Intellectual Property and Ethical Use
Amazon Bedrock Documentation - Responsible Use of Generative AI
NEW QUESTION # 104
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?
Answer: C
Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.
AWS AI Practitioner Reference:
Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.
NEW QUESTION # 105
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
Answer: C
Explanation:
The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Explanation:
Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
References:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest
/userguide/agents.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)
NEW QUESTION # 106
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Answer: A
Explanation:
* Anomaly detection identifies unusual behavior (such as suspicious IP traffic) compared to normal baselines.
* Speech recognition (A) is irrelevant.
* NER in NLP (B) extracts entities from text, not detect malicious IPs.
* Fraud forecasting (D) predicts fraudulent transactions but not directly suspicious IP activity.
# Reference:
AWS Documentation - Anomaly Detection
NEW QUESTION # 107
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