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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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

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 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 (Q66-Q71):

NEW QUESTION # 66
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

A. Amazon Q in Amazon QuickSight
B. Amazon Q in AWS Chatbot
C. Amazon Q Developer
D. Amazon Q in Amazon EC2

Answer: A

NEW QUESTION # 67
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?

A. Ensure that the data is balanced and collected from a diverse group.
B. Use data from only customers who match the demography of the company's overall customer base.
C. Ensure that the data is from a publicly available dataset.
D. Collect data from customers who have a past purchase history.

Answer: A

Explanation:
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
Option A: Use data from only customers who match the demography of the company's overall customer base.
Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)

NEW QUESTION # 68
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?

A. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
B. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
C. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
D. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

Answer: C

Explanation:
To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.
* Custom Service Role Per Team:
* A custom service role for each team ensures that the access control is granular, allowing only specific teams to access their own customer data in Amazon S3.
* This setup aligns with the principle of least privilege, ensuring teams can only interact with data they are authorized to access.
* Why Option A is Correct:
* Access Control: Allows precise access permissions for each team's data.
* Security Compliance: Directly meets the company's security policy requirements by ensuring data segregation.
* Why Other Options are Incorrect:
* B. Custom service role with customer name specification: This approach is impractical as it relies on manual input, which is prone to errors and does not inherently enforce data access controls.
* C. Redacting personal data and updating S3 bucket policy: Redaction does not solve the requirement for team-specific access, and updating bucket policies is less granular than creating roles.
* D. One Bedrock role with full S3 access and IAM roles for teams: This setup does not meet the least privilege principle, as having a single role with full access is contrary to the company's security policy.
Thus, A is the correct answer to meet the company's security requirements.

NEW QUESTION # 69
An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week.
Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?

A. Prompt injection
B. Data leakage
C. Large language model (LLM) hallucinations
D. Concept drift

Answer: A

Explanation:
The ecommerce company's chatbot, powered by AI, automates customer order submissions and is accessible
24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot's behavior, such as bypassing safeguards or accessing unauthorized information.
This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model's behavior, potentially leading to unauthorized actions or harmful outputs.
Implementing guardrails and input validation can mitigate this risk."
(Source: AWS Bedrock User Guide, Security Best Practices)
Detailed Explanation:
* Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot's operation.
* Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
* Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
* Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
References:
AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide
/security.html)
AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)

NEW QUESTION # 70
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

A. Integration with Amazon S3 for object storage
B. Ability to perform real-time analysis on streaming data
C. Support for geospatial indexing and queries
D. Scalable index management and nearest neighbor search capability

Answer: D

Explanation:
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
* Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost- effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
* Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.
* AWS AI Practitioner References:
* According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.
* The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.
The other options do not directly relate to building vector database applications:
* A. Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.
* B. Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.
* D. Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.

NEW QUESTION # 71
......

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