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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Available Languages: | Spanish, Simplified Chinese, Portuguese (Brazil), English, Korean, Japanese |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 3 years |
| Exam Price: | $100 USD |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple response, Multiple choice |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Recommended: basic understanding of cloud computing and general AI/ML concepts. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 188
An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?
Answer: B
Explanation:
Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.
* C is correct:
"Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications." This feature supports creating subtitles and transcripts for media files.
(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide)
* A (Comprehend) is for NLP/text analytics.
* B (Polly) is text-to-speech.
* D (Translate) translates text, but does not create subtitles from audio/video.
NEW QUESTION # 189
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Answer: A
Explanation:
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.
Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner Reference:
Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.
NEW QUESTION # 190
A healthcare company wants to create a model to improve disease diagnostics by analyzing patient voices.
The company has recorded hundreds of patient voices for this project. The company is currently filtering voice recordings according to duration and language.
Answer: A
Explanation:
The correct answer is B - Data preprocessing, which involves cleaning, filtering, and transforming raw data before model training. AWS defines preprocessing as the step where irrelevant or poor-quality data (e.g., excessively short or misrecorded audio) is removed or normalized. The described task-filtering recordings by duration and language-is a textbook example of preprocessing since it prepares the dataset for downstream feature extraction. Feature engineering would occur afterward when converting audio signals into numerical representations (like Mel-frequency coefficients). Model training and data collection come after and before preprocessing respectively. Proper preprocessing ensures data consistency, reduces noise, and improves model accuracy, especially in audio-based healthcare models where clarity and consistency are critical.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - Data Preprocessing and Cleaning
Amazon SageMaker Data Wrangler Documentation - Data Preparation Best Practices
NEW QUESTION # 191
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
Answer:
Explanation:
This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.
Scenario 1:
Encrypt the application data, and isolate the application on a private network.
# Principle: Privacy and Security
From AWS documentation:
" Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance. " Scenario 2:
Evaluate how different population groups will be impacted.
# Principle: Fairness
From AWS documentation:
" The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected. " Scenario 3:
Test the application with unexpected data to ensure the application will work in unique situations.
# Principle: Robustness
From AWS documentation:
" Robustness refers to an AI system's ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases. " Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Practices Whitepaper - Core Principles of Responsible AI Amazon Bedrock Documentation - Responsible AI and Safety Controls AWS Certified Machine Learning Specialty Guide - AI Governance and Model Evaluation
NEW QUESTION # 192
A company is developing its first generative AI application and wants to put a responsible AI policy in place before going to production. The company is concerned with explainability and transparency with model selections for the application.
Which techniques or tools address these issues? (Select TWO.)
Answer: B,D
Explanation:
The verified answers are A. Model evaluation and C. AI model service cards. The question is specifically about explainability and transparency with model selections before a generative AI application goes to production. AWS AI Practitioner guidance identifies model selection as part of the foundation model lifecycle and includes evaluation as a required activity in that lifecycle. AWS also lists model-selection factors such as model type, performance requirements, capabilities, constraints, compliance, modality, latency, model complexity, customization, and input/output length. This means model evaluation directly supports responsible selection because it provides measurable evidence that a model meets the application's business, technical, and governance requirements.
AI model service cards are also correct because AWS describes AI Service Cards as responsible AI documentation that improves transparency. AWS states that AI Service Cards provide information about intended use cases, limitations, responsible AI design choices, and performance optimization best practices for AWS AI services and models. AWS also links these cards to responsible AI areas such as fairness, bias, explainability, robustness, governance, transparency, privacy, and security. That directly addresses the company's concern about transparency when choosing models.
Guardrails is not the best answer here. Guardrails are important for safety controls, denied topics, content filtering, and responsible runtime behavior, but the question focuses on explainability and transparency in model selection, not output filtering. Data encryption is incorrect because encryption protects data confidentiality at rest or in transit; it does not explain model behavior or document model limitations. Automated reasoning is also not the best match. It can support formal validation of specific policy-based logic, but it is not the primary AWS tool or technique for transparent model selection. Therefore, model evaluation and AI model service cards are the two techniques/tools that best address the stated requirement.
NEW QUESTION # 193
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