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>> Valid AIF-C01 Test Answers <<
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NEW QUESTION # 293
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Answer: D
Explanation:
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner References:
Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
NEW QUESTION # 294
What Is the benefit of fine-tuning a foundation model (FM)?
Answer: B
NEW QUESTION # 295
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: B
Explanation:
The verified answer is B. AWS Artifact. AWS Artifact provides on-demand downloads of AWS security and compliance documents, including compliance reports and certifications. AWS documentation also states that AWS Artifact can provide security and compliance documents for independent software vendors who sell products on AWS Marketplace. This directly matches the requirement involving ISV compliance reports.
AWS Artifact documentation further explains that users can access AWS reports on the AWS reports tab and access independent software vendor reports on the Third-party reports tab. This confirms that AWS Artifact is the correct service for obtaining ISV compliance documentation.
The notification requirement also points to AWS Artifact. AWS documentation states that the AWS Artifact console can be used to configure email notifications for updates on agreements and reports in AWS Artifact.
AWS Artifact sends those notifications through AWS User Notifications, and users can specify notification recipients and which notifications they receive.
AWS Audit Manager is incorrect because it helps automate evidence collection and compliance assessments for the customer's AWS usage. It does not primarily provide ISV compliance report notifications. AWS Trusted Advisor is incorrect because it provides best-practice recommendations for AWS accounts, not ISV report availability. AWS Data Exchange is incorrect because it is used to find, subscribe to, and use third- party data products. It is not the compliance report and notification portal.
Therefore, the correct service is AWS Artifact.
NEW QUESTION # 296
A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in. Which data governance strategy will ensure compliance and protect patient privacy?
Answer: C
Explanation:
* Data residency ensures data is stored and processed within specific geographic or jurisdictional boundaries, meeting compliance requirements like HIPAA or GDPR.
* Data quality refers to accuracy and consistency of data.
* Data discoverability is about cataloging and searching datasets.
* Data enrichment enhances datasets with additional external data.
# Reference:
AWS Data Residency Guide
NEW QUESTION # 297
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
Answer: D
Explanation:
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 beingtrained 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.
NEW QUESTION # 298
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