2025 AMAZON AIF-C01: AWS CERTIFIED AI PRACTITIONER PERFECT RELIABLE EXAM GUIDE

2025 Amazon AIF-C01: AWS Certified AI Practitioner Perfect Reliable Exam Guide

2025 Amazon AIF-C01: AWS Certified AI Practitioner Perfect Reliable Exam Guide

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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
  • 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q57-Q62):

NEW QUESTION # 57
Which metric measures the runtime efficiency of operating AI models?

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

Answer: C

Explanation:
The average response time is the correct metric for measuring the runtime efficiency of operating AI models.
* Average Response Time:
* Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.
* A lower average response time indicates a more efficient model that can handle queries quickly.
* Why Option C is Correct:
* Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.
* Performance Indicator: Helps identify potential bottlenecks and optimize model performance.
* Why Other Options are Incorrect:
* A. Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.
* B. Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.
* D. Number of training instances: Refers to data used during training, not operational efficiency.


NEW QUESTION # 58
A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.
Which solution scope gives the company the MOST ownership of security responsibilities?

  • A. Building an application by using an existing third-party generative AI foundation model (FM).
  • B. Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.
  • C. Using a third-party enterprise application that has embedded generative AI features.
  • D. Building and training a generative AI model from scratch by using specific data that a customer owns.

Answer: D

Explanation:
Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.
* Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns": This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.
* Option A: "Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.
* Option B: "Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third-party model provider.
* Option C: "Refining an existing third-party generative AI FM by fine-tuning the model with business- specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.
AWS AI Practitioner References:
* Generative AI Security Scoping Matrix on AWS: AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.


NEW QUESTION # 59
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?

  • A. Reduce the volume of data that is used in training.
  • B. Increase the model training time.
  • C. Increase the volume of data that is used in training.
  • D. Add hyperparameters to the model.

Answer: C


NEW QUESTION # 60
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?

  • A. Agents for Amazon Bedrock
  • B. Amazon Rekognition
  • C. Guardrails for Amazon Bedrock
  • D. Amazon Bedrock playgrounds

Answer: C

Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications.
When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
* Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
* Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
* Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
* Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.
AWS AI Practitioner References:
* Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
* Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.


NEW QUESTION # 61
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?

  • A. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  • B. Ensure that the S3 data does not contain sensitive information.
  • C. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  • D. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.

Answer: D

Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
* Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
* Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
* Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
* Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner References:
* Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.


NEW QUESTION # 62
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