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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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 (Q424-Q429):

NEW QUESTION # 424
A company is building an ML model to analyze archived dat
a. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?

Answer: C

Explanation:
I'll continue with more questions shortly. Stay tuned!


NEW QUESTION # 425
A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock.

Answer: D

Explanation:
* With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).
* Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.
* Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.
# Reference:
AWS Shared Responsibility Model


NEW QUESTION # 426
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.) SageMaker Clarify SageMaker Model Registry SageMaker Serverless Inference

Answer:

Explanation:

Reference:
AWS SageMaker Documentation: Model Registry (https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html) AWS SageMaker Documentation: Serverless Inference (https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-inference.html) AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference) Let's format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications.


NEW QUESTION # 427
A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.
Which combination of AWS service and storage class meets these requirements? (Select TWO.)

Answer: A,D

Explanation:
* AWS CloudTrail: Logs all API calls to Amazon Bedrock.
* Amazon S3 Intelligent-Tiering: Optimizes storage costs for long-term retention with automatic tiering.
According to Amazon Bedrock Logging Documentation:
"CloudTrail records API activity and events, and logs can be stored in S3. For cost optimization, use S3 Intelligent-Tiering to retain logs long-term."


NEW QUESTION # 428
A company wants to create a chatbot to answer employee questions about company policies. Company policies are updated frequently. The chatbot must reflect the changes in near real time. The company wants to choose a large language model (LLM).

Answer: A

Explanation:
The correct answer is C because Retrieval-Augmented Generation (RAG) allows a large language model to provide responses based on up-to-date content from external data sources without the need to fine-tune the model.
According to the AWS Bedrock Developer Guide:
"Amazon Bedrock Knowledge Bases enables developers to augment foundation models (FMs) with company-specific data that is updated in real time or near real time. By separating retrieval from the model itself, RAG-based approaches avoid the need for frequent retraining or fine-tuning." This means a company can use a knowledge base with Amazon Bedrock to dynamically fetch the latest company policy information and feed it to the LLM in the prompt. This approach is ideal for use cases where the content (like policies) changes frequently, and latency for updates must be minimal.
Explanation of other options:
A . Fine-tuning an LLM with SageMaker is not optimal for frequently updated data. Fine-tuning involves retraining and redeploying the model, which is time-consuming and not suited for real-time updates. As stated in the SageMaker documentation:
"Fine-tuning is best used for use cases where the data changes infrequently and where highly specific model behavior is required." B . Selecting a foundation model alone does not fulfill the real-time requirement. The FM's base knowledge is static unless augmented through additional methods like RAG.
D . Amazon Q Business is intended for workplace productivity and enterprise use but is more opinionated in structure and doesn't provide the same flexibility as a custom RAG workflow for building a tailored chatbot application. While it supports some real-time data sync features, it's not purpose-built for LLM-based chat systems with dynamic data feeds like Knowledge Bases in Bedrock.
Therefore, the most appropriate and scalable solution aligned with AWS recommendations is C.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Knowledge Bases and RAG (2024 Edition) AWS Certified Machine Learning Specialty Study Guide - Generative AI Section AWS Documentation: Choosing Between Fine-Tuning and RAG for LLM Applications Amazon SageMaker Documentation - Model Tuning and Deployment Best Practices (2024)


NEW QUESTION # 429
......

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