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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	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 3	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 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	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.

Amazon AWS Certified AI Practitioner Sample Questions (Q177-Q182):

NEW QUESTION # 177
Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

A. PartyRock, an Amazon Bedrock Playground
B. Amazon SageMaker endpoints
C. Amazon SageMaker JumpStart
D. Amazon Personalize

Answer: C

Explanation:
Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.
* Amazon SageMaker JumpStart:
* Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.
* Designed to simplify and accelerate the deployment of machine learning models, including foundation models.
* Why Option B is Correct:
* Rapid Deployment: JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.
* Ease of Use: Provides a user-friendly interface to select and deploy models, reducing the time to value.
* Why Other Options are Incorrect:
* A. Amazon Personalize: Focuses on creating personalized recommendations, not deploying foundation models.
* C. PartyRock: Not a recognized AWS service.
* D. Amazon SageMaker endpoints: Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.

NEW QUESTION # 178
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?

A. Use unsupervised learning to create a model that will estimate probability density.
B. Use supervised learning to create a regression model that will predict probability.
C. Use code that will calculate probability by using simple rules and computations.
D. Use reinforcement learning to train a model to return the probability.

Answer: C

Explanation:
The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.
Option C (Correct): "Use code that will calculate probability by using simple rules and computations": This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.
Option A: "Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.
Option B: "Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.
Option D: "Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.
AWS AI Practitioner Reference:
Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

NEW QUESTION # 179
A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement?

A. Binary data
B. Time series data
C. Text data
D. Image data

Answer: B

Explanation:
Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one- dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.
Reference: Amazon SageMaker DeepAR Algorithm

NEW QUESTION # 180
A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM.
Which solution meets these requirements?

A. Continuous pre-training
B. Latent training
C. Batch learning
D. Static training

Answer: A

Explanation:
To keep a foundation model (FM) relevant with the most recent data, the company needs a training strategy that supports regular updates. Continuous pre-training involves periodically updating a pre-trained model with new data to improve its performance and relevance over time, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Continuous pre-training is a strategy where a pre-trained model is periodically updated with new data to keep it relevant and improve its performance. This approach is commonly used for foundation models to ensure they adapt to new trends and information." (Source: AWS AI Practitioner Learning Path, Module on Model Training Strategies) Detailed Explanation:
* Option A: Batch learningBatch learning involves training a model on a fixed dataset in batches, but it does not inherently support regular updates with new data to keep the model relevant over time.
* Option B: Continuous pre-trainingThis is the correct answer. Continuous pre-training updates the FM with recent data, ensuring it stays relevant by adapting to new trends and information.
* Option C: Static trainingStatic training implies training a model once on a fixed dataset without updates, which does not meet the requirement for regular updates.
* Option D: Latent trainingLatent training is not a standard term in AWS or ML contexts. It may refer to latent space in models like VAEs, but it is not a strategy for regular model updates.
References:
AWS AI Practitioner Learning Path: Module on Model Training Strategies
Amazon Bedrock User Guide: Model Customization and Updates (https://docs.aws.amazon.com/bedrock
/latest/userguide/custom-models.html)
AWS Documentation: Machine Learning Training Strategies (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 181
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

A. Detect imbalances or disparities in the data.
B. Ensure that the model runs frequently.
C. Ensure that the model's inference time is within the accepted limits.
D. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
E. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.

Answer: A,D

NEW QUESTION # 182
......

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