AWS CERTIFIED AI PRACTITIONER AIF-C01 — 2026/2027 | 65 QUESTIONS AND
CORRECT ANSWERS | GRADED A+ | 100% VERIFIED
AWS Certified AI Practitioner (AIF-C01) Certification Examination | Core Domains: Domain 1: AI and
ML Fundamentals (20%) – AI/ML concepts, terminologies, and the machine learning pipeline; Domain
2: Generative AI Fundamentals (24%) – Generative AI concepts, capabilities, limitations, and AWS
infrastructure for generative AI; Domain 3: Applications of Foundation Models (28%) – Foundation
model selection, prompt engineering, model customization, and performance evaluation; Domain 4:
Guidelines for Responsible AI (14%) – Fairness, transparency, explainability, and ethical AI principles;
Domain 5: Security, Compliance, and Governance for AI Solutions (14%) – Data protection, access
control, compliance frameworks, and governance for AI/ML workloads | Key Services Include: Amazon
SageMaker, Amazon Bedrock, Amazon Rekognition, Amazon Comprehend, Amazon Transcribe,
Amazon Translate, Amazon Polly, Amazon Lex, Amazon Personalize, Amazon Forecast, AWS AI Service
Cards, and Amazon SageMaker Ground Truth | Foundational AI/ML Focus | AWS Certification-Aligned
Format
Exam Structure
AWS Certified AI Practitioner (AIF-C01) Examination is commonly structured as follows :
• 65 total questions (50 scored, 15 unscored experimental items)
• Multiple-choice questions (one correct answer) and multiple-response questions (two or more correct
answers)
• Additional question formats may include ordering, matching, and case study items
• Computer-based testing at Pearson VUE testing centers or online with remote proctoring
• 90 minutes to complete the exam
• Passing score: 700 out of 1000 (scaled score)
• Languages: English, Japanese, Korean, Portuguese (Brazil), Simplified Chinese, Arabic, French,
German, Italian, Spanish (Latin America), Spanish (Spain), Traditional Chinese
• Exam fee: $100 USD
Domain Weight Distribution :
• Domain 1: AI and ML Fundamentals – 20%
• Domain 2: Generative AI Fundamentals – 24%
• Domain 3: Applications of Foundation Models – 28% (largest domain)
• Domain 4: Guidelines for Responsible AI – 14%
• Domain 5: Security, Compliance, and Governance for AI Solutions – 14%
Introduction
This AWS Certified AI Practitioner AIF-C01 Exam preparation resource for the 2026/2027 academic cycle
reflects the latest Amazon Web Services certification standards for foundational AI and machine learning
knowledge. Introduced in August 2024, the AWS Certified AI Practitioner certification validates a
candidate's understanding of AI, ML, and generative AI concepts, technologies, and their associated AWS
services and tools. The target audience includes business analysts, IT support professionals, marketing
and sales professionals, product and project managers, and anyone interested in demonstrating
foundational AI/ML knowledge without requiring extensive technical implementation experience. The
examination evaluates a comprehensive understanding of AI/ML fundamentals, generative AI concepts,
foundation model applications, responsible AI principles, and security considerations required for
effective collaboration with technical teams and informed decision-making around AI solutions. Mastery
of AIF-C01 content establishes a solid foundation for advanced AWS AI/ML certifications, including the
,AWS Certified Machine Learning Engineer - Associate and the AWS Certified Generative AI Developer -
Professional.
Answer Format
All questions must be presented in bold text for clear distinction and readability.
All correct answers must be presented in bold and green, followed by clearly defined, technically
accurate rationales that reinforce AI/ML concepts, AWS service applications, responsible AI principles,
and foundational understanding required for AIF-C01 certification success.
Questions Based on AIF-C01 Objectives :
1. A company makes forecasts each quarter to decide how to optimize operations to meet
expected demand. The company uses ML models to make these forecasts. An AI
practitioner is writing a report about the trained ML models to provide transparency and
explainability to company stakeholders. What should the AI practitioner include in the
report to meet the transparency and explainability requirements?
A. Code for model training
B. Partial dependence plots (PDPs)
C. Sample data for training
D. Model convergence tables
B. Partial dependence plots (PDPs)
Rationale: Partial dependence plots (PDPs) show the marginal effect of one or two features on the
predicted outcome of a machine learning model, providing stakeholders with visual transparency into
how different features influence model decisions. This meets explainability requirements by making
model behavior interpretable. Model training code (A) is too technical for stakeholders, sample data (C)
doesn't explain model decisions, and convergence tables (D) relate to training optimization rather than
model explainability .
2. 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?
A. Build an automatic named entity recognition system
B. Create a recommendation engine
C. Develop a summarization chatbot
D. Develop a multi-language translation system
C. Develop a summarization chatbot
Rationale: A summarization chatbot is specifically designed to read documents and extract key points,
providing concise summaries of longer text. Named entity recognition (A) extracts specific entities like
names and dates but doesn't capture key points comprehensively. Recommendation engines (B) suggest
items based on preferences. Translation systems (D) convert between languages but don't summarize
content. For extracting key points from legal documents, summarization is the appropriate solution .
, 3. A company wants to classify human genes into 20 categories based on gene
characteristics. The company needs an ML algorithm to document how the inner
mechanism of the model affects the output. Which ML algorithm meets these
requirements?
A. Decision trees
B. Linear regression
C. Logistic regression
D. Neural networks
A. Decision trees
Rationale: Decision trees provide inherent interpretability through their tree-like structure of decision
rules, making it easy to trace how input features affect classification outcomes. This meets the
requirement for documenting the model's inner mechanisms. Linear regression (B) is for continuous
prediction. Logistic regression (C) is for binary classification. Neural networks (D) are often considered
"black boxes" with limited interpretability, making them less suitable when transparency about inner
mechanisms is required .
4. A company has built an image classification model to predict plant diseases from photos
of plant leaves. The company wants to evaluate how many images the model classified
correctly. Which evaluation metric should the company use to measure the model's
performance?
A. R-squared score
B. Accuracy
C. Root mean squared error (RMSE)
D. Learning rate
B. Accuracy
Rationale: Accuracy measures the proportion of correctly classified images out of all predictions,
making it the appropriate metric for evaluating classification performance. R-squared (A) and RMSE
(C) are regression metrics for continuous value prediction. Learning rate (D) is a training
hyperparameter, not an evaluation metric. For image classification tasks, accuracy directly answers
"how many images were classified correctly" .
5. A company is using a pre-trained large language model (LLM) to build a chatbot for
product recommendations. The company needs the LLM outputs to be short and written in
a specific language. Which solution will align the LLM response quality with the company's
expectations?
A. Adjust the prompt
B. Choose an LLM of a different size
C. Increase the temperature
D. Increase the Top K value
CORRECT ANSWERS | GRADED A+ | 100% VERIFIED
AWS Certified AI Practitioner (AIF-C01) Certification Examination | Core Domains: Domain 1: AI and
ML Fundamentals (20%) – AI/ML concepts, terminologies, and the machine learning pipeline; Domain
2: Generative AI Fundamentals (24%) – Generative AI concepts, capabilities, limitations, and AWS
infrastructure for generative AI; Domain 3: Applications of Foundation Models (28%) – Foundation
model selection, prompt engineering, model customization, and performance evaluation; Domain 4:
Guidelines for Responsible AI (14%) – Fairness, transparency, explainability, and ethical AI principles;
Domain 5: Security, Compliance, and Governance for AI Solutions (14%) – Data protection, access
control, compliance frameworks, and governance for AI/ML workloads | Key Services Include: Amazon
SageMaker, Amazon Bedrock, Amazon Rekognition, Amazon Comprehend, Amazon Transcribe,
Amazon Translate, Amazon Polly, Amazon Lex, Amazon Personalize, Amazon Forecast, AWS AI Service
Cards, and Amazon SageMaker Ground Truth | Foundational AI/ML Focus | AWS Certification-Aligned
Format
Exam Structure
AWS Certified AI Practitioner (AIF-C01) Examination is commonly structured as follows :
• 65 total questions (50 scored, 15 unscored experimental items)
• Multiple-choice questions (one correct answer) and multiple-response questions (two or more correct
answers)
• Additional question formats may include ordering, matching, and case study items
• Computer-based testing at Pearson VUE testing centers or online with remote proctoring
• 90 minutes to complete the exam
• Passing score: 700 out of 1000 (scaled score)
• Languages: English, Japanese, Korean, Portuguese (Brazil), Simplified Chinese, Arabic, French,
German, Italian, Spanish (Latin America), Spanish (Spain), Traditional Chinese
• Exam fee: $100 USD
Domain Weight Distribution :
• Domain 1: AI and ML Fundamentals – 20%
• Domain 2: Generative AI Fundamentals – 24%
• Domain 3: Applications of Foundation Models – 28% (largest domain)
• Domain 4: Guidelines for Responsible AI – 14%
• Domain 5: Security, Compliance, and Governance for AI Solutions – 14%
Introduction
This AWS Certified AI Practitioner AIF-C01 Exam preparation resource for the 2026/2027 academic cycle
reflects the latest Amazon Web Services certification standards for foundational AI and machine learning
knowledge. Introduced in August 2024, the AWS Certified AI Practitioner certification validates a
candidate's understanding of AI, ML, and generative AI concepts, technologies, and their associated AWS
services and tools. The target audience includes business analysts, IT support professionals, marketing
and sales professionals, product and project managers, and anyone interested in demonstrating
foundational AI/ML knowledge without requiring extensive technical implementation experience. The
examination evaluates a comprehensive understanding of AI/ML fundamentals, generative AI concepts,
foundation model applications, responsible AI principles, and security considerations required for
effective collaboration with technical teams and informed decision-making around AI solutions. Mastery
of AIF-C01 content establishes a solid foundation for advanced AWS AI/ML certifications, including the
,AWS Certified Machine Learning Engineer - Associate and the AWS Certified Generative AI Developer -
Professional.
Answer Format
All questions must be presented in bold text for clear distinction and readability.
All correct answers must be presented in bold and green, followed by clearly defined, technically
accurate rationales that reinforce AI/ML concepts, AWS service applications, responsible AI principles,
and foundational understanding required for AIF-C01 certification success.
Questions Based on AIF-C01 Objectives :
1. A company makes forecasts each quarter to decide how to optimize operations to meet
expected demand. The company uses ML models to make these forecasts. An AI
practitioner is writing a report about the trained ML models to provide transparency and
explainability to company stakeholders. What should the AI practitioner include in the
report to meet the transparency and explainability requirements?
A. Code for model training
B. Partial dependence plots (PDPs)
C. Sample data for training
D. Model convergence tables
B. Partial dependence plots (PDPs)
Rationale: Partial dependence plots (PDPs) show the marginal effect of one or two features on the
predicted outcome of a machine learning model, providing stakeholders with visual transparency into
how different features influence model decisions. This meets explainability requirements by making
model behavior interpretable. Model training code (A) is too technical for stakeholders, sample data (C)
doesn't explain model decisions, and convergence tables (D) relate to training optimization rather than
model explainability .
2. 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?
A. Build an automatic named entity recognition system
B. Create a recommendation engine
C. Develop a summarization chatbot
D. Develop a multi-language translation system
C. Develop a summarization chatbot
Rationale: A summarization chatbot is specifically designed to read documents and extract key points,
providing concise summaries of longer text. Named entity recognition (A) extracts specific entities like
names and dates but doesn't capture key points comprehensively. Recommendation engines (B) suggest
items based on preferences. Translation systems (D) convert between languages but don't summarize
content. For extracting key points from legal documents, summarization is the appropriate solution .
, 3. A company wants to classify human genes into 20 categories based on gene
characteristics. The company needs an ML algorithm to document how the inner
mechanism of the model affects the output. Which ML algorithm meets these
requirements?
A. Decision trees
B. Linear regression
C. Logistic regression
D. Neural networks
A. Decision trees
Rationale: Decision trees provide inherent interpretability through their tree-like structure of decision
rules, making it easy to trace how input features affect classification outcomes. This meets the
requirement for documenting the model's inner mechanisms. Linear regression (B) is for continuous
prediction. Logistic regression (C) is for binary classification. Neural networks (D) are often considered
"black boxes" with limited interpretability, making them less suitable when transparency about inner
mechanisms is required .
4. A company has built an image classification model to predict plant diseases from photos
of plant leaves. The company wants to evaluate how many images the model classified
correctly. Which evaluation metric should the company use to measure the model's
performance?
A. R-squared score
B. Accuracy
C. Root mean squared error (RMSE)
D. Learning rate
B. Accuracy
Rationale: Accuracy measures the proportion of correctly classified images out of all predictions,
making it the appropriate metric for evaluating classification performance. R-squared (A) and RMSE
(C) are regression metrics for continuous value prediction. Learning rate (D) is a training
hyperparameter, not an evaluation metric. For image classification tasks, accuracy directly answers
"how many images were classified correctly" .
5. A company is using a pre-trained large language model (LLM) to build a chatbot for
product recommendations. The company needs the LLM outputs to be short and written in
a specific language. Which solution will align the LLM response quality with the company's
expectations?
A. Adjust the prompt
B. Choose an LLM of a different size
C. Increase the temperature
D. Increase the Top K value
