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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 80 |
| Available Languages: | Portuguese (Brazil), Korean, Japanese, English, Simplified Chinese |
| Exam Price: | USD 100 |
| Related Certifications: | AWS Certified Machine Learning Engineer - Associate AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate |
| Passing Score: | 700 / 1000 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple choice, Multiple response |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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問題 #198
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
答案：D
解題說明：
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
* D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
* A (JumpStart) provides prebuilt solutions and models, not monitoring.
* B (HyperPod) is for large-scale training, not model monitoring.
* C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.
問題 #199
Which AI technique combines large language models (LLMs) with external knowledge bases to improve response accuracy?
答案：A
解題說明：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Retrieval Augmented Generation (RAG) enhances LLM responses by:
* Retrieving relevant information from external knowledge sources
* Injecting retrieved content into the prompt context
* Reducing hallucinations and improving factual accuracy
AWS generative AI guidance describes RAG as a best practice when models must use up-to-date or domain- specific knowledge that is not embedded in the model weights.
Why the other options are incorrect:
* RL (A) focuses on reward-based learning.
* NLP (B) is a broad field, not a specific technique.
* Transfer learning (D) adapts model weights but does not retrieve external data at inference time.
AWS AI document references:
* Retrieval Augmented Generation on AWS
* Improving LLM Accuracy with External Knowledge
* Generative AI Architectures
問題 #200
A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?
答案：B
解題說明：
RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date. Reference: AWS RAG Techniques.
問題 #201
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?
答案：C
解題說明：
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 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.
Reference:
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
問題 #202
A company is exploring Amazon Nova models in Amazon Bedrock. The company needs a multimodal model that supports multiple languages.
答案：A
解題說明：
Comprehensive and Detailed
Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports text, images, and multiple languages.
Nova Lite is optimized for lightweight, faster inference at lower cost.
Nova Canvas is a creative tool for visual design.
Nova Reel is optimized for video-related use cases.
Reference:
AWS Documentation - Amazon Nova Models
問題 #203
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P.S. Testpdf在Google Drive上分享了免費的、最新的AIF-C01考試題庫：https://drive.google.com/open?id=1W5tFyj3q1oGrQIViLKEr8bX3EiXW6pVM