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[General] New AIF-C01 Test Syllabus | AIF-C01 Visual Cert Exam

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【General】 New AIF-C01 Test Syllabus | AIF-C01 Visual Cert Exam

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Amazon AIF-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q253-Q258):NEW QUESTION # 253
A company needs to use Amazon SageMaker AI for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access.
Which solution will meet these requirements?
  • A. Encrypt the data at rest by using encryption for SageMaker geospatial capabilities.
  • B. Associate appropriate AWS Identity and Access Management (IAM) roles with the SageMaker jobs.
  • C. Run SageMaker training and inference by using SageMaker Experiments.
  • D. Run SageMaker training and inference by using network isolation.
Answer: D
Explanation:
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a VPC and are not accessible from the internet. Per the official SageMaker documentation:
"When you enable network isolation, your model can't make any outbound network calls. This is useful for security and regulatory compliance when working with sensitive data."

NEW QUESTION # 254
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?
  • A. Bilingual Evaluation Understudy (BLEU)
  • B. F1 score
  • C. Root mean squared error (RMSE)
  • D. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
Answer: A

NEW QUESTION # 255
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.
Which factor relates to the explainability of the AI solution's decisions?
  • A. Model complexity
  • B. Training time
  • C. Number of hyperparameters
  • D. Deployment time
Answer: A
Explanation:
The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions.
Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like
"black boxes."
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes." (Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify) Detailed Explanation:
* Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.
* Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.
* Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.
* Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.
References:
Amazon SageMaker Developer Guide: Explainability with SageMaker Clarify (https://docs.aws.amazon.com
/sagemaker/latest/dg/clarify-explainability.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Explainability AWS Documentation: Explainable AI (https://aws.amazon.com/machine-learning/responsible-ai/)

NEW QUESTION # 256
A financial company is using ML to help with some of the company's tasks.
Which option is a use of generative AI models?
  • A. Summarizing customer complaints
  • B. Segmenting customers based on type of investments
  • C. Classifying customers based on product usage
  • D. Forecasting revenue for certain products
Answer: A
Explanation:
Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text-like customer complaints-is a classic application of generative AI.
A is correct:
"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text." (Reference: AWS Generative AI Use Cases) B and C are standard ML classification/segmentation tasks.
D is a regression/prediction task, not generative.

NEW QUESTION # 257
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?
  • A. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
  • B. Select a large, diverse dataset to pre-train a new generative model.
  • C. Create effective prompts that provide clear instructions and context to guide the model's generation.
  • D. Increase the model's complexity by adding more layers to the model's architecture.
Answer: C

NEW QUESTION # 258
......
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