Amazon
AIF-C01
AWS Certified AI Practitioner Exam
Exam Version: 22.7
Questions & Answers PDF
(Demo Version - Limited Content)
For More Information - Visit link below:
https://p2pexam.com/aif-c01
, Question 1. (Single Select)
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?
A: Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom
model.
B: Mask the confidential data in the inference responses by using dynamic data masking.
C: Encrypt the confidential data in the inference responses by using Amazon SageMaker.
D: Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
Answer: A
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
https://p2pexam.com/aif-c01 Page 2 of 9
AIF-C01
AWS Certified AI Practitioner Exam
Exam Version: 22.7
Questions & Answers PDF
(Demo Version - Limited Content)
For More Information - Visit link below:
https://p2pexam.com/aif-c01
, Question 1. (Single Select)
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?
A: Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom
model.
B: Mask the confidential data in the inference responses by using dynamic data masking.
C: Encrypt the confidential data in the inference responses by using Amazon SageMaker.
D: Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
Answer: A
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
https://p2pexam.com/aif-c01 Page 2 of 9
