AIF-C01 Exam Dumps - AWS Certified AI Practitioner Exam(AI1-C01)

To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.

Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference":This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.

Option A:"Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.

Option C:"Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.

Option D:"Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.

AWS AI Practitioner References:

Amazon SageMaker Endpoints:AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.

When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.

Explanation of Each Option:

Option A (Correct):"Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.

Option B:"Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model being trained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training.

Option C:"Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.

Option D:"Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.

AWS AI Practitioner References:

Data Handling Best Practices in AWS Machine Learning:AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.

Amazon Bedrock and Model Training Security:Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.

Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.

Amazon SageMaker Feature Store:

A fully managed repository for storing, sharing, and managing machine learning features across different teams and models.

It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy.

Why Option A is Correct:

Centralized Feature Management:Provides a central repository for managing features, making it easier to share them across teams.

Collaboration and Reusability:Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch.

Why Other Options are Incorrect:

B. SageMaker Data Wrangler:Helps with data preparation and analysis but does not provide a centralized feature store.

C. SageMaker Clarify:Used for bias detection and explainability, not for managing variables across teams.

D. SageMaker Model Cards:Provide model documentation, not feature management.

Experimenting and refining the prompt is the best approach to ensure that the chatbot using a foundation model (FM) produces responses that adhere to the company's tone.

Prompt Engineering:

Adjusting and refining the prompt allows for better control over the FM's outputs, ensuring they align with the desired tone and style.

This iterative process involves testing different prompts and modifying them based on the model's responses to achieve the desired outcome.

Why Option C is Correct:

Directly Influences Output Quality:Allows for fine-tuning of the model’s responses to match the company's tone.

Cost-Effective:Does not require modifying the model itself, only the inputs to it.

Why Other Options are Incorrect:

A. Low limit on tokens:Limits response length but not the adherence to company tone.

B. Batch inferencing:Relates to processing multiple inputs, not controlling response tone.

D. Higher temperature:Increases randomness in responses, which could deviate from the desired tone.

Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.

Amazon SageMaker JumpStart:

Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.

Designed to simplify and accelerate the deployment of machine learning models, including foundation models.

Why Option B is Correct:

Rapid Deployment:JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.

Ease of Use:Provides a user-friendly interface to select and deploy models, reducing the time to value.

Why Other Options are Incorrect:

A. Amazon Personalize:Focuses on creating personalized recommendations, not deploying foundation models.

C. PartyRock:Not a recognized AWS service.

D. Amazon SageMaker endpoints:Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.

Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.

Fine-Tuning Strategy:

To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.

This allows the model to learn the desired behavior or response style based on the provided examples.

Why Option A is Correct:

Proper Training Format:The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.

Customization:Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.

Why Other Options are Incorrect:

B. Prepare a .txt file in .csv format:This does not align with the specific need for labeled data with prompts and completions.

C. Purchase Provisioned Throughput:Relates to read/write capacity in databases, not to model fine-tuning.

D. Train on journals and textbooks:Lacks the specific format and labeling required for fine-tuning.

Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.

Sampling Bias:

Occurs when the training data is not representative of the broader population, leading to skewed model outputs.

In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.

Why Option B is Correct:

Reflects Data Imbalance:A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.

Common Issue in ML Models:Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.

Why Other Options are Incorrect:

A. Measurement bias:Involves errors in data collection or measurement, not sampling.

C. Observer bias:Refers to bias introduced by researchers or data collectors, not the model's output.

D. Confirmation bias:Involves favoring information that confirms existing beliefs, not relevant to model output bias.

To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.

Option C (Correct): "Data protection":This is correct because data protection is critical for compliance with privacy and security regulations.

Option B (Correct): "Threat detection":This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.

Option A:"Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.

Option D:"Cost optimization" is incorrect because it is focused on managing expenses, not compliance.

Option E:"Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.

AWS AI Practitioner References:

AWS Compliance Capabilities:AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.