AIF-C01 Übungsfragen: AWS Certified AI Practitioner & AIF-C01 Dateien Prüfungsunterlagen

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Amazon AIF-C01 Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 3
  • 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.
Thema 4
  • 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.
Thema 5
  • 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.

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Amazon AWS Certified AI Practitioner AIF-C01 Prüfungsfragen mit Lösungen (Q364-Q369):

364. Frage
An AI practitioner has trained a model on a training dataset. The model performs well on the training data. However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?

Antwort: B

Begründung:
When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.
* Overfitting: The model memorizes the training data patterns instead of generalizing.
* Underfitting (A) means the model performs poorly on both training and test data.
* Bias (C) refers to systemic errors in predictions, not this training/test mismatch.
* Prompt engineering (B) applies to generative AI, not general ML training models.
# Reference:
AWS ML Glossary - Overfitting and Underfitting


365. Frage
A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.
Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times. (Select THREE.)
* Batch transform
* Real-time inference

Antwort:

Begründung:

Explanation:
Use Case 1:
The company's chatbot needs predictions from the LLM to understand users' intent with minimal latency.
Chatbots require low-latency, immediate responses to user input. Real-time inference is ideal for these interactive use cases.
Use Case 2:
A data processing job needs to query the LLM to process gigabytes of text files on weekends.
Batch transform is designed for asynchronous, high-throughput jobs where latency is not critical. It's ideal for scheduled or large-scale processing like weekend batch jobs on large datasets.
Use Case 3:
The company's engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions.
An API requiring fast response time for small content sizes is best served by real-time inference to meet latency requirements.


366. Frage
Which option is a characteristic of AI governance frameworks for building trust and deploying human- centered AI technologies?

Antwort: B

Begründung:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Explanation:
* Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
* Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
* Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
* Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
References:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)


367. Frage
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?

Antwort: A


368. Frage
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?

Antwort: C


369. Frage
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