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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 2
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 3
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 4
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 5
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

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Amazon AWS Certified AI Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning

Answer:

Explanation:

Explanation:

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)


NEW QUESTION # 50
A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?

Answer: D

Explanation:
The correct answer is C because Amazon Nova Reel is the AWS foundation model designed for generative video use cases, providing end-to-end video generation using generative AI, which significantly reduces video creation time and eliminates the need for manual assembly.
According to AWS Bedrock documentation:
"Amazon Nova Reel enables users to generate short-form video content directly from prompts, including the ability to define style, motion, scenes, and transitions - streamlining the generative content creation process." This is the most operationally efficient choice as it does not require stitching together images or using external editing tools.
Explanation of other options:
A and B involve generating intermediate images and then manually creating videos using video editing tools
- not operationally efficient.
D). Amazon Nova Pro is intended for high-end professional-grade image or 3D content generation, but not specifically optimized for video generation like Nova Reel.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Model Directory - Nova Models Overview
* AWS GenAI Foundation Model Comparison Guide
* AWS Generative AI for Creators Whitepaper (2024)


NEW QUESTION # 51
Which approach provides human-in-the-loop improvement of foundation models (FMs) throughout the ML lifecycle?

Answer: D

Explanation:
The verified answer is C. Incorporating continuous human feedback across model development, training, and deployment phases and using performance evaluation to improve model accuracy . The question asks for human-in-the-loop improvement throughout the ML lifecycle , not a one-time review and not fully automated correction. AWS AI Practitioner guidance describes the foundation model lifecycle as including data selection, model selection, pre-training, fine-tuning, evaluation, deployment, and feedback.
Because feedback is part of the lifecycle, human review should not be limited to the first training stage only.
AWS generative AI lifecycle guidance also emphasizes continuous improvement and feedback loops. It explains that operationalizing a generative AI application is not a one-time event, but an ongoing process driven by real-world usage, feedback systems, automated feedback loops, direct user feedback mechanisms, and strategic human-in-the-loop interventions. This supports option C because it combines human feedback across phases with performance evaluation to improve model behavior and accuracy over time.
Option A is incorrect because it removes human intervention. Automated testing is valuable, but it is not human-in-the-loop. Option B is incorrect because collecting human feedback only during initial training is too narrow. It ignores post-training evaluation, deployment monitoring, user feedback, drift, and iterative improvement. Option D is incorrect because it relies on automated reinforcement learning and predefined metrics without human oversight. That again contradicts the "human-in-the-loop" requirement.
The strongest clue is the phrase "throughout the ML lifecycle." A correct human-in-the-loop strategy must include human review, human feedback, and evaluation during development, training, deployment, and ongoing improvement. Therefore, option C is the only answer that satisfies both parts of the requirement:
continuous human involvement and lifecycle-based model improvement.


NEW QUESTION # 52
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters

Answer:

Explanation:


NEW QUESTION # 53
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 # 54
......

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