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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Price: | USD 100 |
| Real Exam Qty: | 80 |
| Exam Duration: | 90 minutes |
| Available Languages: | Japanese, English, Portuguese (Brazil), Korean, Simplified Chinese |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple choice, Multiple response |
| Passing Score: | 700 / 1000 |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate AWS Certified Machine Learning Engineer - Associate |
| 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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NEW QUESTION # 309
Sentiment analysis is a subset of which broader field of AI?
Answer: C
Explanation:
* Sentiment analysis is the task of determining the emotional tone or intent behind a body of text (positive, negative, neutral).
* This falls under Natural Language Processing (NLP) because it deals with understanding and processing human language.
* Computer vision relates to images, robotics to autonomous machines, and time series forecasting to predicting values from sequential data.
# Reference:
AWS ML Glossary - NLP
NEW QUESTION # 310
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. 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.)
Answer: C,D
Explanation:
To build and use an AI model responsibly, especially in sensitive applications like loan approvals, it's crucial to address potential biases and ensure transparency:
* Detect imbalances or disparities in the data (Option A): Analyzing the training data for imbalances or disparities is essential. Imbalanced data can lead to models that are biased towards the majority class, potentially disadvantaging certain groups. By identifying and mitigating these imbalances, the company can reduce the risk of biased predictions.
* Evaluate the model's behavior to provide transparency to stakeholders (Option C): Regularly assessing the model's outputs and decision-making processes allows the company to understand how decisions are made. This evaluation fosters transparency, enabling the company to explain model behavior to stakeholders and ensure that the model operates as intended without unintended biases.
Options B, D, and E, while relevant to model performance and evaluation, do not directly address the responsible use of AI concerning bias and transparency.
NEW QUESTION # 311
Which option is a characteristic of AI governance frameworks for building trust and deploying human- centered AI technologies?
Answer: A
Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Explanation:
* Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
* Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
* Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
* Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
References:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
NEW QUESTION # 312
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
Answer: A
Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
* Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
* Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
* Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
* Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
* Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
NEW QUESTION # 313
An online learning company with large volumes of education materials wants to use enterprise search.
Answer: D
Explanation:
The correct answer is C - Amazon Kendra, AWS's enterprise search service designed for organizations with large, diverse document repositories. Kendra uses machine learning and natural language understanding (NLU) to provide semantic search, meaning it retrieves results based on meaning rather than keyword matching. According to AWS documentation, Kendra is ideal for educational, enterprise, and knowledge- management scenarios where users need fast, accurate retrieval across PDFs, HTML, Office documents, FAQs, and multimedia transcripts. Kendra connectors can index content from S3, SharePoint, LMS platforms, and internal databases, making it perfect for large volumes of training materials. Amazon Comprehend (A) is for NLP tasks like entity extraction, not enterprise search. Amazon Textract (B) extracts text from PDFs and scanned materials but does not provide search capabilities. Amazon Personalize (D) is for personalized recommendations, not document retrieval. Kendra is purpose-built for enterprise search and aligns directly with the company's needs.
Referenced AWS Documentation:
* Amazon Kendra Developer Guide - Enterprise Search
* AWS ML Specialty Guide - Intelligent Search Systems
NEW QUESTION # 314
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