利用AIF-C01真題 -不用擔心AWS Certified AI Practitioner

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Amazon AIF-C01 考試大綱：

主題	簡介
主題 1	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.
主題 2	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.
主題 3	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.
主題 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.
主題 5	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.

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最新的 AWS Certified AI AIF-C01 免費考試真題 (Q107-Q112):

問題 #107
A food service company wants to collect a dataset to predict customer food preferences. The company wants to ensure that the food preferences of all demographics are included in the data.

A. Diversity
B. Reliability
C. Recency bias
D. Accuracy

答案：A

解題說明：
Comprehensive and Detailed
Diversity in datasets ensures representation of all demographics, reducing bias and improving fairness.
Accuracy is model performance.
Recency bias skews towards recent data.
Reliability measures consistency of results, not representation.
Reference:
AWS Responsible AI Guidelines - Data Diversity

問題 #108
A company has an ML model. The company wants to know how the model makes predictions. Which term refers to understanding model predictions?

A. Model interoperability
B. Model interpretability
C. Model training
D. Model performance

答案：B

解題說明：
The correct answer is A because model interpretability refers to the ability to understand and explain how an ML model arrives at a particular prediction or decision.
From AWS documentation:
"Model interpretability is the degree to which a human can understand the cause of a decision made by a machine learning model. Interpretability techniques help explain which features influenced a prediction and how much they contributed." This is essential in areas like financial services, healthcare, or compliance-heavy industries, where decision transparency is critical.
Explanation of other options:
B . Model training refers to the process of teaching a model from data and doesn't explain how predictions are made.
C . Model interoperability refers to the ability of systems or models to work across different platforms or environments.
D . Model performance refers to how accurate or effective the model is but doesn't relate to the explanation of its decisions.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Explainability and Bias Detection AWS Machine Learning Specialty Study Guide - Responsible AI and Model Explainability

問題 #109
An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Adjust the temperature parameter of the model.
B. Include fairness metrics for model evaluation.
C. Modify the training data to mitigate bias.
D. Avoid overfitting on the training data.
E. Apply prompt engineering techniques.

答案：B,C

解題說明：
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
* A. Include Fairness Metrics for Model Evaluation:
* Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
* These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
* C. Modify the Training Data to Mitigate Bias:
* Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
* Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
* Why Other Options are Incorrect:
* B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
* D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
* E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

問題 #110
A company wants to learn about generative AI applications in an experimental environment. Which solution will meet this requirement MOST cost-effectively?

A. Amazon Bedrock PartyRock
B. Amazon Q Developer
C. Amazon SageMaker JumpStart
D. Amazon Q Business

答案：A

解題說明：
Comprehensive and Detailed
Amazon Bedrock PartyRock is a free, no-code playground for experimenting with generative AI apps.
SageMaker JumpStart is powerful but incurs costs.
Q Developer and Q Business are enterprise tools, not experimental learning environments.
Reference:
AWS Documentation - PartyRock

問題 #111
A company wants to build an ML model to detect abnormal patterns in sensor data. The company does not have labeled data for training. Which ML method will meet these requirements?

A. Classification
B. Decision tree
C. Linear regression
D. Autoencoders

答案：D

解題說明：
The correct answer is D because autoencoders are an unsupervised machine learning method commonly used for anomaly detection when labeled data is not available.
From AWS documentation:
"Autoencoders learn to compress and reconstruct input data. During anomaly detection, they learn normal patterns in data. Data points that the model cannot accurately reconstruct are flagged as anomalies." This approach is ideal when there is no labeled data and when patterns must be learned based on normal behavior alone - a common situation in IoT sensor data environments.
Explanation of other options:
A). Linear regression requires labeled data and is used for predicting continuous values.
B). Classification requires labeled data to assign inputs into categories.
C). Decision trees are supervised learning models and also require labeled datasets.
Referenced AWS AI/ML Documents and Study Guides:
* AWS Machine Learning Specialty Guide - Unsupervised Learning Techniques
* Amazon SageMaker Examples - Anomaly Detection Using Autoencoders

問題 #112
......

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利用AIF-C01真題 -不用擔心AWS Certified AI Practitioner

Amazon AIF-C01 考試大綱：

AIF-C01下載 - AIF-C01 PDF題庫

最新的 AWS Certified AI AIF-C01 免費考試真題 (Q107-Q112):

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