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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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.
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	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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q102-Q107):

NEW QUESTION # 102
Which phase of the ML lifecycle determines compliance and regulatory requirements?

A. Feature engineering
B. Business goal identification
C. Data collection
D. Model training

Answer: B

Explanation:
The business goal identification phase of the ML lifecycle involves defining the objectives of the project and understanding the requirements, including compliance and regulatory considerations. This phase ensures the ML solution aligns with legal and organizational standards before proceeding to technical stages like data collection or model training.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The business goal identification phase involves defining the problem to be solved, identifying success metrics, and determining compliance and regulatory requirements to ensure the ML solution adheres to legal and organizational standards." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
* Option A: Feature engineeringFeature engineering involves creating or selecting features for model training, which occurs after compliance requirements are identified. It does not address regulatory concerns.
* Option B: Model trainingModel training focuses on building the ML model using data, not on determining compliance or regulatory requirements.
* Option C: Data collectionData collection involves gathering data for training, but compliance and regulatory requirements (e.g., data privacy laws) are defined earlier in the business goal identification phase.
* Option D: Business goal identificationThis is the correct answer. This phase ensures that compliance and regulatory requirements are considered at the outset, shaping the entire ML project.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: ML Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg
/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)

NEW QUESTION # 103
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem

Answer:

Explanation:

Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-mlconcepts.html) AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)

NEW QUESTION # 104
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?

A. Continued pre-training
B. Unsupervised learning
C. Fine-tuning
D. Reinforcement learning from human feedback (RLHF)

Answer: C

Explanation:
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide)
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.

NEW QUESTION # 105
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?

A. Increase the volume of data that is used in training.
B. Reduce the volume of data that is used in training.
C. Add hyperparameters to the model.
D. Increase the model training time.

Answer: A

NEW QUESTION # 106
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?

A. Track the model changes by using Amazon Fraud Detector.
B. Track the model changes by using Git.
C. Track the model changes by using Amazon SageMaker Model Cards.
D. Track the model changes by using Amazon Comprehend.

Answer: C

NEW QUESTION # 107
......

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Amazon AWS Certified AI Practitioner Sample Questions (Q102-Q107):

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