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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

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 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

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 (Q157-Q162):

NEW QUESTION # 157
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

A. Enable voice commands for coding and providing natural language search.
B. Run an application without provisioning or managing servers.
C. Create software snippets, reference tracking, and open-source license tracking.
D. Convert audio files to text documents by using ML models.

Answer: C

Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

NEW QUESTION # 158
A company wants to create a new solution by using AWS Glue. The company has minimal programming experience with AWS Glue.
Which AWS service can help the company use AWS Glue?

A. AWS Config
B. Amazon Personalize
C. Amazon Comprehend
D. Amazon Q Developer

Answer: D

Explanation:
AWS Glue is a serverless data integration service that enables users to extract, transform, and load (ETL) data. For a company with minimal programming experience, Amazon Q Developer provides an AI-powered assistant that can generate code, explain AWS services, and guide users through tasks like creating AWS Glue jobs. This makes it an ideal tool to help the company use AWS Glue effectively.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Q Developer:
"Amazon Q Developer is an AI-powered assistant that helps developers by generating code, answering questions about AWS services, and providing step-by-step guidance for tasks such as building ETL pipelines with AWS Glue. It is designed to assist users with varying levels of expertise, including those with minimal programming experience." (Source: AWS Documentation, Amazon Q Developer Overview) Detailed Option A: Amazon Q Developer This is the correct answer. Amazon Q Developer can assist the company by generating AWS Glue scripts, explaining Glue concepts, and providing guidance on setting up ETL jobs, which is particularly helpful for users with limited programming experience.
Option B: AWS Config
AWS Config is used for tracking and managing resource configurations and compliance, not for assisting with coding or using services like AWS Glue. This option is incorrect.
Option C: Amazon Personalize
Amazon Personalize is a machine learning service for building recommendation systems, not for assisting with data integration or AWS Glue. This option is irrelevant.
Option D: Amazon Comprehend
Amazon Comprehend is an NLP service for analyzing text, not for helping users write code or use AWS Glue. This option does not meet the requirements.
Reference:
AWS Documentation: Amazon Q Developer Overview (https://aws.amazon.com/q/developer/) AWS Glue Developer Guide: Introduction to AWS Glue (https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) AWS AI Practitioner Learning Path: Module on AWS Developer Tools and Services

NEW QUESTION # 159
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

A. Enable voice commands for coding and providing natural language search.
B. Run an application without provisioning or managing servers.
C. Convert audio files to text documents by using ML models.
D. Create software snippets, reference tracking, and open-source license tracking.

Answer: A

NEW QUESTION # 160
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?

A. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
B. Deploy optimized large language models (LLMs) on edge devices.
C. Deploy optimized small language models (SLMs) on edge devices.
D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer: C

Explanation:
To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and havefaster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.
Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices": This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.
Option B: "Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.
Option C: "Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.
Option D: "Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.
AWS AI Practitioner Reference:
Optimizing AI Models for Edge Devices on AWS: AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

NEW QUESTION # 161
A company wants to keep its foundation model (FM) relevant by using the most recent dat a. The company wants to implement a model training strategy that includes regular updates to the FM.
Which solution meets these requirements?

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

Answer: D

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 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.
Reference:
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 # 162
......

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