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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
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	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 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)

A. Amazon Polly
B. Amazon Rekognition
C. Amazon Bedrock
D. Amazon Lex
E. Amazon Comprehend

Answer: C,E

Explanation:
To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.
* Amazon Bedrock:
* Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.
* The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces like chatbots, not for sentiment analysis.
* C. Amazon Polly: Converts text to speech; it doesn't analyze sentiment.
* E. Amazon Rekognition: Analyzes images and videos, not text.

NEW QUESTION # 44
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.
Which methodology should the company use to meet these requirements?

A. Reinforcement learning from human feedback (RLHF)
B. Supervised learning
C. Unsupervised learning
D. Reinforcement learning

Answer: C

Explanation:
Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.
* Unsupervised Learning:
* This type of machine learning is used when the data has no labels or pre-defined categories. The goal is to identify patterns, clusters, or associations within the data.
* In this case, the company has petabytes of unlabeled customer data and needs to classify customers into different tiers. Unsupervised learning techniques like clustering (e.g., K-Means, Hierarchical Clustering) can group similar customers based on various attributes without any prior knowledge or labels.
* Why Option B is Correct:
* Handling Unlabeled Data: Unsupervised learning is specifically designed to work with unlabeled data, making it ideal for the company's need to classify customer data.
* Customer Segmentation: Techniques in unsupervised learning can be used to find natural groupings within customer data, such as identifying high-value vs. low-value customers or segmenting based on purchasing behavior.
* Why Other Options are Incorrect:
* A. Supervised learning: Requires labeled data with input-output pairs to train the model, which is not suitable since the company's data is unlabeled.
* C. Reinforcement learning: Focuses on training an agent to make decisions by maximizing some notion of cumulative reward, which does not align with the company's need for customer classification.
* D. Reinforcement learning from human feedback (RLHF): Similar to reinforcement learning but involves human feedback to refine the model's behavior; it is also not appropriate for classifying unlabeled customer data.

NEW QUESTION # 45
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?

A. Toxicity
B. Hallucinations
C. Privacy
D. Plagiarism

Answer: D

NEW QUESTION # 46
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential dat a. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?

A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
B. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
C. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
D. Mask the confidential data in the inference responses by using dynamic data masking.

Answer: A

NEW QUESTION # 47
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

A. Number of tokens consumed
B. Total training time
C. Amount of data used to train the LLM
D. Temperature value

Answer: A

Explanation:
In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.
* Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.
* Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.
* Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.
* Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.
AWS AI Practitioner References:
* Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

NEW QUESTION # 48
......

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AIF-C01 Latest Test Simulator, Pdf AIF-C01 Version

PrepAwayETE Enables You to Succeed on The AIF-C01 Exam the First Time

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Amazon AWS Certified AI Practitioner Sample Questions (Q43-Q48):

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