Exam AIF-C01 Objectives, Valid AIF-C01 Real Test

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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	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	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.

Amazon AWS Certified AI Practitioner Sample Questions (Q53-Q58):

NEW QUESTION # 53
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?

A. Configure model invocation logging in Amazon EventBridge.
B. Enable invocation logging in Amazon Bedrock.
C. Configure AWS Audit Manager as the logs destination for the model.
D. Configure AWS CloudTrail as the logs destination for the model.

Answer: B

NEW QUESTION # 54
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. Register the model with the Amazon SageMaker Model Registry.
B. Purchase Provisioned Throughput for the custom model.
C. Grant access to the custom model in Amazon Bedrock.
D. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

Answer: D

NEW QUESTION # 55
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Which solution meets these requirements?

A. Create a fraud forecasting system.
B. Develop an anomaly detection system.
C. Create a natural language processing (NLP) named entity recognition system.
D. Build a speech recognition system.

Answer: B

Explanation:
An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.
* Anomaly Detection:
* Anomaly detection uses machine learning algorithms to identify deviations from normal behavior, such as unexpected traffic from a suspicious IP address.
* This is a common approach for identifying potential threats or malicious activity in cybersecurity applications.
* Why Option C is Correct:
* Detects Suspicious Behavior: An anomaly detection system can monitor and detect IP addresses that exhibit unusual or suspicious patterns.
* Real-time Monitoring: Provides continuous analysis of network traffic to identify potential security threats.
* Why Other Options are Incorrect:
* A. Speech recognition system: Is unrelated to detecting suspicious IP addresses.
* B. NLP named entity recognition: Focuses on identifying entities in text, not IP address analysis.
* D. Fraud forecasting system: Generally used for predicting fraud, not directly applicable to identifying suspicious IPs.
Thus, C is the correct answer for detecting suspicious IP addresses.

NEW QUESTION # 56
Which metric measures the runtime efficiency of operating AI models?

A. Customer satisfaction score (CSAT)
B. Average response time
C. Number of training instances
D. Training time for each epoch

Answer: B

NEW QUESTION # 57
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?

A. Amazon Bedrock Agents
B. Amazon Bedrock Guardrails
C. Amazon Bedrock custom models
D. Amazon Bedrock inference APIs

Answer: B

Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
* Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
* Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
* Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
* Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine- tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Building Safe AI Applications (https://aws.amazon.com/bedrock/)

NEW QUESTION # 58
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Exam AIF-C01 Objectives, Valid AIF-C01 Real Test

Valid AIF-C01 Real Test & New Braindumps AIF-C01 Book

Amazon AIF-C01 Exam Syllabus Topics:

Amazon AWS Certified AI Practitioner Sample Questions (Q53-Q58):

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