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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of Generative AI24%- Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG)
- Explain capabilities and use cases of generative AI
- Define generative AI and foundation models
- Differentiate between generative AI and traditional ML
Applications of Foundation Models28%- Explain integration of foundation models into applications
- Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan
- Recognize tools for building and deploying generative AI solutions
- Describe use cases for text, image, video, and code generation
Fundamentals of AI and ML20%- Identify types of ML: supervised, unsupervised, reinforcement learning
- Recognize key concepts: data, models, training, inference, evaluation
- Define artificial intelligence (AI), machine learning (ML), and deep learning
- Describe common ML workflows and lifecycle
Guidelines for Responsible AI14%- Explain bias detection and reduction
- Define responsible AI principles: fairness, transparency, privacy, safety
- Identify risks and mitigation strategies for AI systems
- Describe ethical and societal impacts of AI
Security, Compliance, and Governance for AI Solutions14%- Data protection and privacy in AI workflows
- Compliance requirements and regulations
- Governance frameworks for AI lifecycle
- Security controls for AI data and models

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Amazon AWS Certified AI Practitioner Sample Questions (Q232-Q237):

NEW QUESTION # 232
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.

Answer: A

Explanation:
The correct answer is A, as the most effective way to ensure fairness in AI systems is to audit and diversify training data. AWS Responsible AI documentation stresses that bias originates primarily from unbalanced or incomplete datasets. By including samples from all demographic groups, the model learns patterns that generalize fairly across populations. AWS SageMaker Clarify supports data bias detection and offers fairness metrics like demographic parity and equal opportunity difference. Deeper models (option B) or secrecy (option C) do not mitigate bias and may worsen transparency. Static test datasets (option D) fail to capture evolving data distributions. Therefore, ongoing bias reviews and diverse datasets form the foundation of responsible generative AI for financial use cases.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Whitepaper - Fairness and Bias Mitigation
Amazon SageMaker Clarify Documentation - Bias Detection


NEW QUESTION # 233
An AI practitioner wants to generate more diverse and more creative outputs from a large language model (LLM).
How should the AI practitioner adjust the inference parameter?

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The temperature parameter controls randomness in model outputs.
AWS generative AI guidance explains:
Higher temperature → more randomness and creativity
Lower temperature → more deterministic and predictable outputs
To increase diversity and creativity, the temperature should be increased.
Why the other options are incorrect:
Lower Top K (B) reduces output diversity.
Response length (C) affects size, not creativity.
Prompt length (D) does not directly control randomness.
AWS AI document references:
Inference Parameters for Foundation Models
Controlling Creativity in LLMs
Text Generation Configuration on AWS


NEW QUESTION # 234
HOTSPOT
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML

Answer:

Explanation:


NEW QUESTION # 235
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?

Answer: D

Explanation:
The manufacturing company needs to create product descriptions in multiple languages, which requires automated language translation. Amazon Translate is a fully managed service that uses machine learning to provide high-quality translation between languages, making it the ideal solution for this task.
Exact Extract from AWS AI Documents:
From the Amazon Translate Developer Guide:
"Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. It can be used to automatically translate text, such as product descriptions, into multiple languages to reach a global audience." (Source: Amazon Translate Developer Guide, Introduction to Amazon Translate) Detailed Explanation:
* Option A: Amazon TranslateThis is the correct answer. Amazon Translate automates the translation of text into multiple languages, directly addressing the company's need to create product descriptions in different languages.
* Option B: Amazon TranscribeAmazon Transcribe converts speech to text, which is unrelated to translating text into multiple languages. This option is incorrect.
* Option C: Amazon KendraAmazon Kendra is an intelligent search service that uses machine learning to provide answers from documents, not for translating text. This option is irrelevant.
* Option D: Amazon PollyAmazon Polly is a text-to-speech service that generates spoken audio from text, not for translating text into other languages. This option does not meet the requirements.
References:
Amazon Translate Developer Guide: Introduction to Amazon Translate (https://docs.aws.amazon.com
/translate/latest/dg/what-is.html)
AWS AI Practitioner Learning Path: Module on Natural Language Processing Services AWS Documentation: Language Translation with Amazon Translate (https://aws.amazon.com/translate/)


NEW QUESTION # 236
A company wants to set up private access to Amazon Bedrock APIs from the company's AWS account. The company also wants to protect its data from internet exposure.

Answer: C

Explanation:
Comprehensive and Detailed
AWS PrivateLink enables private connectivity between your VPC and supported AWS services (like Amazon Bedrock) without sending traffic over the public internet.
CloudFront (A) is for CDN and content delivery, not private service connections.
AWS Glue (B) is for ETL/data catalog, not networking.
Lake Formation (C) provides governance for data lakes, not API network isolation.
Reference:
AWS Documentation - Access Amazon Bedrock with PrivateLink


NEW QUESTION # 237
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