Amazon Web Services Updated AIF-C01 Exam Questions and Answers by akram

Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.

(Reference: Amazon Bedrock Fine-Tuning)

Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.

(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide)

Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model’s language understanding or generation for that domain.

(Reference: Amazon Bedrock Customization Options)

The ecommerce company wants to improve search engine recommendations by customizing results for each user. Amazon Personalize is a machine learning service that enables personalized recommendations, tailoring search results or product suggestions based on individual user behavior and preferences, making it the best fit for this requirement.

Exact Extract from AWS AI Documents:

From the Amazon Personalize Developer Guide:

"Amazon Personalize enables developers to build applications with personalized recommendations, such as customized search results or product suggestions, by analyzing user behavior and preferences to deliver tailored experiences."

(Source: Amazon Personalize Developer Guide, Introduction to Amazon Personalize)

Detailed Explanation:

Option A: Amazon PersonalizeThis is the correct answer. Amazon Personalize specializes in creating personalized recommendations, ideal for customizing search results for each user on an ecommerce platform.

Option B: Amazon KendraAmazon Kendra is an intelligent search service for enterprise data, focusing on retrieving relevant documents or answers, not on personalizing search results for individual users.

Option C: Amazon RekognitionAmazon Rekognition is for image and video analysis, such as object detection or facial recognition, and is unrelated to search engine recommendations.

Option D: Amazon TranscribeAmazon Transcribe converts speech to text, which is not relevant for improving search engine recommendations.

Comprehensive and Detailed Explanation from AWS AI Documents:

Amazon Bedrock ensures data privacy and security by not sharing customer inputs or outputs with third-party model providers.

The models are accessed via Bedrock’s API isolation layer, meaning that model providers do not see your data.

Customer data is not used for training or improving foundation models unless customers explicitly opt in.

From AWS Docs:

“Amazon Bedrock does not share your inputs and outputs with third-party model providers. Your data remains private, and is not used to improve the foundation models.”

This ensures full data privacy, especially for sensitive use cases like confidential documents.

???? Reference:

AWS Documentation – Data privacy in Amazon Bedrock

The correct answers are A and E because both directly align with AWS best practices for securing generative AI services and data privacy in enterprise applications.

From the AWS Amazon Q Business documentation:

"AWS Key Management Service (KMS) integrates with Amazon Q Business to encrypt sensitive data at rest. You can use customer-managed KMS keys to meet compliance requirements."

And:

"You must configure IAM access controls to manage which users and applications can access Amazon Q Business indexes, ensuring that only authorized users can retrieve information."

Explanation of other options:

B. Cross-account access is not a common requirement for internal enterprise use of Amazon Q Business unless explicitly sharing data across organizations. It’s not a requirement for securing access.

C. Amazon Inspector is a vulnerability management tool for EC2 and containers. It is unrelated to Amazon Q authentication or security.

D. Allowing public access would violate security and privacy principles and directly contradict the stated requirement.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Q Business Developer Guide – Security and Identity Management

AWS KMS Documentation – Integration with Bedrock and Amazon Q

AWS Certified Machine Learning Specialty Guide – Responsible AI and Governance Section