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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
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	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 3	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 4	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 5	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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q151-Q156):

NEW QUESTION # 151
Which phase of the ML lifecycle determines compliance and regulatory requirements?

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

Answer: C

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 # 152
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?

A. Root mean squared error (RMSE)
B. F1 score
C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
D. Bilingual Evaluation Understudy (BLEU)

Answer: D

Explanation:
BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.
Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.
Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.
Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.
Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.
AWS AI Practitioner Reference:
Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

NEW QUESTION # 153
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

A. Overfitting
B. Hallucination
C. Data leakage
D. Underfitting

Answer: B

Explanation:
In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problemoften occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.
Option B (Correct): "Hallucination": This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.
Option A: "Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.
Option C: "Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.
Option D: "Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.
AWS AI Practitioner Reference:
Large Language Models on AWS: AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.

NEW QUESTION # 154
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

A. Grant access to the custom model in Amazon Bedrock.
B. Register the model with the Amazon SageMaker Model Registry.
C. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
D. Purchase Provisioned Throughput for the custom model.

Answer: C

Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
* Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
* Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
* Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real- time inference.
* Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
* Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.

NEW QUESTION # 155
Which scenario represents a practical use case for generative AI?

A. Implementing a rule-based recommendation engine to suggest products to customers
B. Employing a chatbot to provide human-like responses to customer queries in real time
C. Using an analytics dashboard to track website traffic and user behavior
D. Using an ML model to forecast product demand

Answer: B

Explanation:
Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human-like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries." (Source: AWS Bedrock User Guide, Introduction to Generative AI) Detailed Option A: Using an ML model to forecast product demandForecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content.
Option B: Employing a chatbot to provide human-like responses to customer queries in real timeThis is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human-like responses, making this a practical use case.
Option C: Using an analytics dashboard to track website traffic and user behaviorAn analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content.
Option D: Implementing a rule-based recommendation engine to suggest products to customersA rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case.
Reference:
AWS Bedrock User Guide: Introduction to Generative AI (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Generative AI Applications AWS Documentation: Generative AI Use Cases (https://aws.amazon.com/generative-ai/)

NEW QUESTION # 156
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NEW AIF-C01 TEST BRAINDUMPS & FREE AIF-C01 DOWNLOAD PDF

New AIF-C01 Test Braindumps & Free AIF-C01 Download Pdf