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New AIF-C01 Test Guide | AIF-C01 Latest Dumps Ppt

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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

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 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

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 5

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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q123-Q128):

NEW QUESTION # 123
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?

A. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
B. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
C. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
D. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.

Answer: B

NEW QUESTION # 124
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 Lex
B. Amazon Polly
C. Amazon Bedrock
D. Amazon Rekognition
E. Amazon Comprehend

Answer: C,E

NEW QUESTION # 125
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?

A. Use Amazon SageMaker AI to build the application by training a new ML model.
B. Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.
C. Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
D. Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.

Answer: C

Explanation:
The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Explanation:
* Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.
Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
* Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
* Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
* Option D: Use Amazon SageMaker AI to build the application by training a new ML model.
Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
References:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest
/userguide/agents.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)

NEW QUESTION # 126
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?

A. Data augmentation by using an Amazon Bedrock knowledge base
B. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
C. Image recognition by using Amazon Rekognition
D. Data summarization by using Amazon QuickSight

Answer: B

Explanation:
Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.
* Amazon SageMaker Ground Truth Plus:
* It allows for the creation of high-quality training datasets with human oversight, which minimizes errors in labeling and increases accuracy.
* Human-in-the-loop workflows help verify the correctness of annotations, ensuring that generated images for protective eyewear meet high-quality standards.
* Why Option A is Correct:
* High Accuracy: Human-in-the-loop validation provides the ability to catch and correct errors in annotations, ensuring high-quality data.
* Minimized Risk of Incorrect Annotations: Human review adds a layer of quality assurance, which is especially important in use cases like generating precise images for protective eyewear.
* Why Other Options are Incorrect:
* B. Amazon Bedrock: Does not offer a knowledge base for data augmentation; it focuses on running foundation models.
* C. Amazon Rekognition: Provides image recognition and analysis, not a solution for minimizing annotation errors.
* D. Amazon QuickSight: A data visualization tool, not relevant to image annotation or generation tasks.
Thus, A is the correct answer for generating high-accuracy images with minimized annotation risks.

NEW QUESTION # 127
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.
Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

A. Content moderation guidelines
B. Moderation logs
C. User-generated content
D. Benchmark datasets

Answer: D

NEW QUESTION # 128
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