HOT AIF-C01 LATEST MOCK TEST 100% PASS | HIGH PASS-RATE AMAZON AWS CERTIFIED AI PRACTITIONER EXAM QUIZZES PASS FOR SURE

HOT AIF-C01 Latest Mock Test 100% Pass | High Pass-Rate Amazon AWS Certified AI Practitioner Exam Quizzes Pass for sure

HOT AIF-C01 Latest Mock Test 100% Pass | High Pass-Rate Amazon AWS Certified AI Practitioner Exam Quizzes Pass for sure

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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
  • 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q81-Q86):

NEW QUESTION # 81
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)

  • A. Cost optimization
  • B. Threat detection
  • C. Data protection
  • D. Loosely coupled microservices
  • E. Auto scaling inference endpoints

Answer: B,C

Explanation:
To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.
Option C (Correct): "Data protection": This is correct because data protection is critical for compliance with privacy and security regulations.
Option B (Correct): "Threat detection": This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.
Option A: "Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.
Option D: "Cost optimization" is incorrect because it is focused on managing expenses, not compliance.
Option E: "Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.
AWS AI Practitioner Reference:
AWS Compliance Capabilities: AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.


NEW QUESTION # 82
A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?

  • A. Build custom models for image classification and recognition.
  • B. Use a deep learning neural network to perform speech recognition.
  • C. Use generative AI summarization to generate human-like text.
  • D. Build ML models to search for patterns in numeric data.

Answer: B

Explanation:
The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language.
AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Transcribe:
"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe) Detailed Explanation:
* Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.
* Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.
* Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.
* Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.
References:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com
/transcribe/latest/dg/what-is.html)
Amazon Polly Developer Guide: Text-to-Speech Overview (https://docs.aws.amazon.com/polly/latest/dg
/what-is.html)
AWS AI Practitioner Learning Path: Module on Speech Recognition and Synthesis


NEW QUESTION # 83
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

  • A. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
  • B. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
  • C. Ensure that the model's inference time is within the accepted limits.
  • D. Ensure that the model runs frequently.
  • E. Detect imbalances or disparities in the data.

Answer: A,E


NEW QUESTION # 84
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

  • A. Set a low limit on the number of tokens the FM can produce.
  • B. Define a higher number for the temperature parameter.
  • C. Use batch inferencing to process detailed responses.
  • D. Experiment and refine the prompt until the FM produces the desired responses.

Answer: D


NEW QUESTION # 85
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?

  • A. Use unsupervised learning.
  • B. Use transfer learning.
  • C. Decrease the number of epochs.
  • D. Increase the number of epochs.

Answer: B

Explanation:
Transfer learning is the correct strategy for adapting pre-trained models for new, related tasks without creating models from scratch.
* Transfer Learning:
* Involves taking a pre-trained model and fine-tuning it on a new dataset for a related task.
* This approach is efficient because it leverages existing knowledge from a model trained on a large dataset, requiring less data and computational resources than training a new model from scratch.
* Why Option B is Correct:
* Adaptation of Pre-trained Models: Allows for adapting existing models to new tasks, which aligns with the company's goal of not starting from scratch.
* Efficiency and Speed: Speeds up the model development process by building on the knowledge of pre-trained models.
* Why Other Options are Incorrect:
* A. Increase the number of epochs: Does not address the strategy of reusing pre-trained models.
* C. Decrease the number of epochs: Similarly, does not apply to adapting pre-trained models.
* D. Use unsupervised learning: Does not involve using pre-trained models for new tasks.


NEW QUESTION # 86
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