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Amazon AIF-C01 Exam Overview:

Certification Vendor:	Amazon AWS
Exam Name:	AWS Certified AI Practitioner
Exam Number:	AIF-C01
Related Certifications:	AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty
Exam Price:	100 USD
Certificate Validity Period:	3 years
Available Languages:	Traditional Chinese, Simplified Chinese, English, Korean, Japanese
Passing Score:	700 (scaled score 100–1000)
Exam Duration:	90 minutes
Exam Format:	Ordering, Multiple choice, Multiple response
Real Exam Qty:	65 (50 scored, 15 unscored)
Recommended Training:	AWS Skill Builder - AI Practitioner Learning Path AWS Certified AI Practitioner Official Training
Exam Registration:	AWS Certification Registration Pearson VUE Scheduling
Sample Questions:	Amazon AIF-C01 Sample Questions
Exam Way:	Online proctored or testing center delivery
Pre Condition:	No required prerequisites; recommended basic understanding of cloud computing and general IT concepts
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html

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Amazon AIF-C01 시험요강:

주제	소개
주제 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.
주제 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.
주제 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.
주제 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.
주제 5	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.

최신 AWS Certified AI AIF-C01 무료샘플문제 (Q68-Q73):

질문 # 68
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.

A. Configure model invocation logging in Amazon EventBridge.
B. Configure AWS Audit Manager as the logs destination for the model.
C. Configure AWS CloudTrail as the logs destination for the model.
D. Enable model invocation logging in Amazon Bedrock.

정답：D

설명：
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

질문 # 69
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?

A. Text embedding model
B. Multi-modal generation model
C. Multi-modal embedding model
D. Image generation model

정답：C

질문 # 70
How can companies use large language models (LLMs) securely on Amazon Bedrock?

A. Enable Amazon Bedrock automatic model evaluation jobs.
B. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
C. Enable AWS Audit Manager for automatic model evaluation jobs.
D. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.

정답：D

설명：
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.
Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management.
Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.
Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.
Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.
AWS AI Practitioner Reference:
Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

질문 # 71
A healthcare company wants to create a model to improve disease diagnostics by analyzing patient voices. The company has recorded hundreds of patient voices for this project. The company is currently filtering voice recordings according to duration and language.

A. Data preprocessing
B. Data collection
C. Feature engineering
D. Model training

정답：A

설명：
The correct answer is B - Data preprocessing, which involves cleaning, filtering, and transforming raw data before model training. AWS defines preprocessing as the step where irrelevant or poor-quality data (e.g., excessively short or misrecorded audio) is removed or normalized. The described task-filtering recordings by duration and language-is a textbook example of preprocessing since it prepares the dataset for downstream feature extraction. Feature engineering would occur afterward when converting audio signals into numerical representations (like Mel-frequency coefficients). Model training and data collection come after and before preprocessing respectively. Proper preprocessing ensures data consistency, reduces noise, and improves model accuracy, especially in audio-based healthcare models where clarity and consistency are critical.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - Data Preprocessing and Cleaning
Amazon SageMaker Data Wrangler Documentation - Data Preparation Best Practices

질문 # 72
What is continued pre-training?

A. The process of fine-tuning a pre-trained language model on labeled data for a specific task
B. The process of evaluating the performance of a pre-trained language model on a test set
C. The process of providing unlabeled data to a pre-trained language model to improve the model's domain knowledge
D. The process of training a language model from the beginning on a specific dataset

정답：C

설명：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Continued pre-training involves training an already pre-trained model further using unlabeled, domain- specific data.
AWS generative AI guidance explains that continued pre-training:
* Expands domain knowledge
* Preserves general language understanding
* Differs from fine-tuning, which uses labeled data
Why the other options are incorrect:
* A describes fine-tuning.
* C describes training from scratch.
* D describes evaluation.
AWS AI document references:
* Foundation Model Customization Techniques
* Continued Pre-Training vs Fine-Tuning
* Domain Adaptation on AWS

질문 # 73
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