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[General] Updated AIF-C01 - Exam AWS Certified AI Practitioner Quick Prep

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【General】 Updated AIF-C01 - Exam AWS Certified AI Practitioner Quick Prep

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Amazon AIF-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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.
Topic 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.
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
  • 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q332-Q337):NEW QUESTION # 332
What does an F1 score measure in the context of foundation model (FM) performance?
  • A. Model speed in generating responses
  • B. Financial cost of operating the model
  • C. Model precision and recall
  • D. Energy efficiency of the model's computations
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The F1 score is a standard evaluation metric that represents the harmonic mean of precision and recall.
In AWS ML evaluation guidance:
* Precision measures correctness of positive predictions
* Recall measures coverage of actual positive cases
* F1 score balances both metrics into a single performance indicator
This makes the F1 score particularly useful when evaluating classification performance of foundation models.
Why the other options are incorrect:
* Speed (B) is a latency metric.
* Cost (C) measures operational efficiency.
* Energy efficiency (D) is unrelated to predictive accuracy.
AWS AI document references:
* Model Evaluation Metrics on AWS
* Classification Performance Measurement
* Amazon SageMaker Evaluation Best Practices

NEW QUESTION # 333
A company is developing an ML model to support the company's retail application. The company wants to use information that the model has produced from previous tasks to increase the learning speed of the model.
Which model training solution will meet these requirements?
  • A. Transfer learning
  • B. Regularization techniques
  • C. Supervised learning
  • D. Hyperparameter tuning
Answer: A
Explanation:
Transfer learning is a machine learning technique that reuses knowledge learned from previous tasks to improve training efficiency and performance on new tasks. AWS documentation explains that transfer learning allows models to start from pretrained weights or representations, reducing training time and the amount of data required.
In this retail application scenario, the company wants to leverage information from prior tasks to increase learning speed, which is a defining characteristic of transfer learning. AWS emphasizes that transfer learning is especially effective when tasks are related, such as customer behavior analysis, product recommendations, or demand forecasting.
By initializing a model with learned features from an existing task, transfer learning enables faster convergence and improved accuracy compared to training from scratch. AWS frequently recommends this approach when computational efficiency and rapid iteration are important.
The other options do not satisfy the requirement. Supervised learning defines how labels are used but does not reuse prior knowledge. Hyperparameter tuning optimizes model configuration but does not leverage previous task outputs. Regularization techniques reduce overfitting but do not accelerate learning through knowledge reuse.
AWS documentation positions transfer learning as a foundational concept in modern ML workflows, particularly for retail, personalization, and natural language processing use cases. Therefore, transfer learning is the correct solution.

NEW QUESTION # 334
A company is developing an ML model to predict heart disease risk. The model uses patient data, such as age, cholesterol, blood pressure, smoking status, and exercise habits. The dataset includes a target value that indicates whether a patient has heart disease.
Which ML technique will meet these requirements?
  • A. Reinforcement learning
  • B. Semi-supervised learning
  • C. Unsupervised learning
  • D. Supervised learning
Answer: D
Explanation:
This scenario describes a classic supervised learning problem. Supervised learning involves training a model on a labeled dataset where the correct output (also called the target variable or ground truth) is already known.
In this case, the company has a dataset containing features such as age, cholesterol, and exercise habits, and a label indicating whether a patient has heart disease or not. This matches the definition of a binary classification task, which is a type of supervised learning. According to the AWS Machine Learning Specialty Study Guide, supervised learning is most effective when labels are available for all training examples, and the goal is to map input variables to a known output. This method enables the model to learn patterns and predict the presence or absence of heart disease. By contrast, unsupervised learning is used when labels are not available, and reinforcement learning involves agents learning via rewards and punishments, which is not applicable here. Semi-supervised learning is used when only some data is labeled.
Referenced AWS AI/ML Documents and Study Guides:
AWS Certified Machine Learning Specialty Guide - Supervised vs. Unsupervised Learning Amazon SageMaker Documentation - Classification Models

NEW QUESTION # 335
Sometimes generative AI models generate data unrelated to the input or the task.
Which term is used for this disadvantage of using generative AI for business problems?
  • A. Nondeterminism
  • B. Interpretability
  • C. Hallucinations
  • D. Data bias
Answer: C
Explanation:
AWS documentation identifies hallucinations as a known limitation of generative AI models, particularly when used in business and production environments. Hallucinations occur when a model generates outputs that are unrelated, incorrect, fabricated, or unsupported by the input data or provided context. These outputs often appear confident and fluent, which can make them difficult to detect without additional validation.
Generative AI models, including large language models, operate using probabilistic token prediction based on patterns learned during training. AWS explains that these models do not have true reasoning or factual grounding unless explicitly provided with context or external knowledge. As a result, when prompts are ambiguous, incomplete, or outside the model's training distribution, the model may produce responses that are irrelevant or misleading.
This behavior presents a risk for business use cases such as customer support, reporting, or decision-making systems. AWS highlights hallucinations as a key challenge that must be mitigated through techniques such as Retrieval Augmented Generation (RAG), prompt engineering, human review, and output validation.
The other options are not correct. Interpretability refers to the ability to understand model decisions, not incorrect outputs. Data bias relates to skewed or unfair training data. Nondeterminism refers to variability in outputs, not relevance or correctness.
AWS consistently categorizes hallucinations as a primary disadvantage of generative AI, making this the correct answer.

NEW QUESTION # 336
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?
  • A. Function calling
  • B. Access controls
  • C. Knowledge bases
  • D. Guardrails
Answer: D
Explanation:
Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system.
Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content.
Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs.
Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated.
Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedr ... ide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/)

NEW QUESTION # 337
......
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