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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

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 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

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 (Q203-Q208):

NEW QUESTION # 203
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning

Answer:

Explanation:

NEW QUESTION # 204
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions. The company wants to measure the direct impact of the AI assistant on sales performance.

A. Natural language understanding accuracy rates
B. The number of customer interactions with the AI assistant
C. The conversion rate of customers who purchase products after AI assistant interactions
D. Sentiment analysis scores from customer feedback after AI assistant interactions

Answer: C

Explanation:
Comprehensive and Detailed
The most direct business KPI for sales performance is conversion rate (percentage of users who purchase after AI assistant interaction).
Number of interactions (B) shows engagement, not sales impact.
Sentiment analysis (C) shows customer satisfaction but not revenue impact.
NLU accuracy (D) is a technical metric, not a business outcome.
Reference:
AWS Generative AI Use Cases - Measuring Business Value

NEW QUESTION # 205
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?

A. Evaluate the models by using a human workforce and custom prompt datasets.
B. Evaluate the models by using built-in prompt datasets.
C. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
D. Use public model leaderboards to identify the model.

Answer: A

NEW QUESTION # 206
Which phase of the ML lifecycle determines compliance and regulatory requirements?

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

Answer: D

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 # 207
Which prompting attack directly exposes the configured behavior of a large language model (LLM)?

A. Ignoring the prompt template
B. Exploiting friendliness and trust
C. Prompted persona switches
D. Extracting the prompt template

Answer: D

Explanation:
* A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).
* An attack that reveals or leaks this prompt template is known as a prompt extraction attack.
* The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.
# Reference:
AWS Responsible AI - Prompt Injection & Extraction Attacks

NEW QUESTION # 208
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