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Amazon AIF-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified AI Practitioner
Exam Number:AIF-C01
Exam Price:USD 100
Certificate Validity Period:3 years
Exam Format:Multiple response, Multiple choice
Passing Score:700 / 1000
Exam Duration:90 minutes
Real Exam Qty:80
Related Certifications:AWS Certified Machine Learning Engineer - Associate
AWS Certified Data Engineer - Associate
AWS Certified Cloud Practitioner
Available Languages:Simplified Chinese, Japanese, Korean, English, Portuguese (Brazil)
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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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
  • 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 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
  • 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 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 (Q444-Q449):

NEW QUESTION # 444
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce.
Which AWS service or feature meets these requirements?

Answer: C

Explanation:
* Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
* Data Wrangler is for data preparation and transformation.
* Transcribe is for speech-to-text.
* Macie is for sensitive data discovery, not labeling.
# Reference:
AWS Documentation - SageMaker Ground Truth Plus


NEW QUESTION # 445
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model

Answer:

Explanation:

Explanation:

Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.
Reference: AWS Generative AI Overview, Diffusion Models Explained - AWS Blog Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Reference: AWS Generative AI on Bedrock, Transformers Explained - AWS
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
Reference: AWS Rekognition Object Detection, Object Detection Overview - AWS


NEW QUESTION # 446
A company has fine-tuned an Amazon Bedrock foundation model (FM) to produce short document summaries. The company wants an automated metric that compares each model-generated summary with its human-written reference summary.
Which metric will meet these requirements?

Answer: B

Explanation:
AWS documentation identifies ROUGE as a standard evaluation metric for text summarization tasks.
ROUGE measures the overlap of n-grams, sequences, or word pairs between a machine-generated summary and a human-written reference summary, making it ideal for automated evaluation.
In this scenario, the company needs a metric that directly compares generated summaries with human references. ROUGE scores quantify how much of the reference content is captured by the model output, which aligns with AWS recommendations for evaluating summarization quality.
The other metrics are not appropriate. F1 score is typically used for classification tasks. Perplexity measures language model confidence but does not compare outputs to reference summaries. Frechet Inception Distance is used for image generation tasks, not text.
AWS consistently recommends ROUGE for summarization evaluation, making it the correct choice.


NEW QUESTION # 447
An online learning company with large volumes of educational materials wants to use enterprise search. Which AWS service meets these requirements?

Answer: D

Explanation:
* Amazon Kendra is an enterprise search service that uses machine learning to index and search unstructured data such as documents, manuals, and course materials.
* Amazon Comprehend performs NLP tasks like sentiment analysis and entity recognition, not enterprise search.
* Amazon Textract extracts structured text from scanned docs and PDFs.
* Amazon Personalize builds recommendation systems, not search engines.
# Reference:
AWS Documentation - Amazon Kendra


NEW QUESTION # 448
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

Answer: D


NEW QUESTION # 449
......

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