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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
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
  • 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.
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 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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Amazon AWS Certified AI Practitioner Sample Questions (Q179-Q184):

NEW QUESTION # 179
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.)
* Few-shot learning
* Fine-tuning
* Retrieval Augmented Generation (RAG)
* Zero-shot learning

Answer:

Explanation:

Explanation:

AWS References:
* Amazon Bedrock - Retrieval Augmented Generation (RAG)
* AWS Generative AI Guide - Zero-shot & Few-shot learning


NEW QUESTION # 180
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
Which human-centered design principle does this scenario present?

Answer: A

Explanation:
Explainability refers to the ability of an AI system to make its decision-making process clear and understandable to humans.
A is correct:
"Explainability is crucial for human-centered AI, especially in healthcare, to ensure that doctors and patients understand the rationale behind AI-driven recommendations." (Reference: AWS Responsible AI) B relates to protecting data, not explanations.
C is about treating groups equally.
D is about managing data lifecycle, not providing rationales.


NEW QUESTION # 181
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)

Answer: C,D

Explanation:
To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.
Option C (Correct): "Data protection": This is correct because data protection is critical for compliance with privacy and security regulations.
Option B (Correct): "Threat detection": This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.
Option A: "Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.
Option D: "Cost optimization" is incorrect because it is focused on managing expenses, not compliance.
Option E: "Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.
AWS AI Practitioner Reference:
AWS Compliance Capabilities: AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.


NEW QUESTION # 182
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?

Answer: A

Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Explanation:
* Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
* Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
* Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
* Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
References:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 183
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.

Answer: D

Explanation:
The correct answer is B - Enable model invocation logging in Amazon Bedrock. AWS Bedrock provides native functionality to log all model invocations, including input prompts, parameters, and generated outputs, to Amazon CloudWatch Logs or S3. According to AWS documentation, this feature helps developers monitor model usage, analyze errors, and audit for compliance. Unlike CloudTrail or Audit Manager, which record API events and compliance data respectively, invocation logging captures real inference transactions.
EventBridge is used for event routing, not persistent log storage. Bedrock's logging feature ensures complete traceability for debugging, usage analytics, and governance in accordance with Responsible AI principles.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Model Invocation Logging
AWS Responsible AI Documentation - Monitoring and Auditability


NEW QUESTION # 184
......

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