1
AWS Certified AI
Practitioner (AIF-C01) —
Advanced Practice Exam
v2.0 150 Multiple-Choice
Questions with Answers
and Rationales a well
detailed one 2025 /
2026 written and graded
A+ upgraded
, 2
Domain 1: Fundamentals of AI and ML (20%) — Questions 1–30
Question 1
A data science team is building a model to predict customer churn for a subscription-based
service. The dataset contains 10,000 customer records with features including subscription
length, monthly charges, usage patterns, and support interactions. The target variable is a
binary flag indicating whether the customer churned in the last month. Which type of machine
learning problem is this?
A. Regression
B. Binary classification
C. Multi-class classification
D. Clustering
-” detailed answer 100 % correct :-”B
Rationale: Predicting a binary outcome (churned or not churned) is a binary classification
problem. Regression (A) predicts continuous values; multi-class classification (C) predicts more
than two categories; clustering (D) finds patterns in unlabeled data.
Question 2
Which statement correctly differentiates between artificial intelligence (AI), machine learning
(ML), and deep learning (DL)?
A. AI is a subset of ML, which is a subset of DL
B. DL is a subset of ML, which is a subset of AI
C. ML, DL, and AI are three unrelated disciplines
D. AI and ML are the same; DL is different
-” detailed answer 100 % correct :-”B
, 3
Rationale: Machine learning is a subset of artificial intelligence. Deep learning is a subset of
machine learning that uses neural networks with multiple layers. This hierarchical relationship is
fundamental to understanding AI concepts.
Question 3
A hospital system has implemented a machine learning model to predict patient readmission
risk within 30 days of discharge. The model uses features such as age, diagnosis, number of
previous admissions, and medication adherence. The model achieves 92% accuracy on the test
set. What does this accuracy score indicate?
A. The model correctly predicts 92% of all readmissions
B. The model correctly classifies 92% of all cases in the test set
C. The model has a 92% precision rate
D. The model explains 92% of the variance
-” detailed answer 100 % correct :-”B
Rationale: Accuracy measures the proportion of correct predictions (both true positives and
true negatives) out of all predictions made. It does not specifically measure readmission
prediction (A), precision (C), or variance explained (D).
Question 4
A retail company wants to segment its customer base into distinct groups based on purchasing
behavior without any predefined categories. Which machine learning technique is most
appropriate?
A. Classification
B. Regression
C. Clustering
D. Reinforcement learning
-” detailed answer 100 % correct :-”C
Rationale: Clustering is an unsupervised learning technique used to discover natural groupings
in unlabeled data. Since the company has no predefined categories, unsupervised clustering is
appropriate. Classification (A) requires labeled data; regression (B) predicts continuous values;
reinforcement learning (D) uses reward-based learning.
, 4
Question 5
A model trained to predict housing prices achieves an R² score of 0.89 on the test set. What
does this indicate?
A. The model correctly classifies 89% of houses
B. The model explains 89% of the variance in housing prices
C. The model has 89% precision in its predictions
D. The model has an error rate of 11%
-” detailed answer 100 % correct :-”B
Rationale: R² (coefficient of determination) measures the proportion of variance in the
dependent variable that is predictable from the independent variables. An R² of 0.89 means
89% of the variance in housing prices is explained by the model.
Question 6
Which of the following is an example of a real-world AI application?
A. Sorting a list of numbers
B. Fraud detection in financial transactions
C. Calculating the sum of two numbers
D. Alphabetizing a list of names
-” detailed answer 100 % correct :-”B
Rationale: Fraud detection is a common AI/ML application that involves learning patterns from
data to identify anomalies. Sorting and arithmetic operations (A, C, D) are deterministic
algorithmic tasks that do not require machine learning.
Question 7
What is the primary difference between batch inference and real-time inference?
A. Batch inference is always more accurate
B. Real-time inference provides immediate predictions; batch inference processes data in bulk
C. Batch inference requires less data
D. Real-time inference is always more expensive
AWS Certified AI
Practitioner (AIF-C01) —
Advanced Practice Exam
v2.0 150 Multiple-Choice
Questions with Answers
and Rationales a well
detailed one 2025 /
2026 written and graded
A+ upgraded
, 2
Domain 1: Fundamentals of AI and ML (20%) — Questions 1–30
Question 1
A data science team is building a model to predict customer churn for a subscription-based
service. The dataset contains 10,000 customer records with features including subscription
length, monthly charges, usage patterns, and support interactions. The target variable is a
binary flag indicating whether the customer churned in the last month. Which type of machine
learning problem is this?
A. Regression
B. Binary classification
C. Multi-class classification
D. Clustering
-” detailed answer 100 % correct :-”B
Rationale: Predicting a binary outcome (churned or not churned) is a binary classification
problem. Regression (A) predicts continuous values; multi-class classification (C) predicts more
than two categories; clustering (D) finds patterns in unlabeled data.
Question 2
Which statement correctly differentiates between artificial intelligence (AI), machine learning
(ML), and deep learning (DL)?
A. AI is a subset of ML, which is a subset of DL
B. DL is a subset of ML, which is a subset of AI
C. ML, DL, and AI are three unrelated disciplines
D. AI and ML are the same; DL is different
-” detailed answer 100 % correct :-”B
, 3
Rationale: Machine learning is a subset of artificial intelligence. Deep learning is a subset of
machine learning that uses neural networks with multiple layers. This hierarchical relationship is
fundamental to understanding AI concepts.
Question 3
A hospital system has implemented a machine learning model to predict patient readmission
risk within 30 days of discharge. The model uses features such as age, diagnosis, number of
previous admissions, and medication adherence. The model achieves 92% accuracy on the test
set. What does this accuracy score indicate?
A. The model correctly predicts 92% of all readmissions
B. The model correctly classifies 92% of all cases in the test set
C. The model has a 92% precision rate
D. The model explains 92% of the variance
-” detailed answer 100 % correct :-”B
Rationale: Accuracy measures the proportion of correct predictions (both true positives and
true negatives) out of all predictions made. It does not specifically measure readmission
prediction (A), precision (C), or variance explained (D).
Question 4
A retail company wants to segment its customer base into distinct groups based on purchasing
behavior without any predefined categories. Which machine learning technique is most
appropriate?
A. Classification
B. Regression
C. Clustering
D. Reinforcement learning
-” detailed answer 100 % correct :-”C
Rationale: Clustering is an unsupervised learning technique used to discover natural groupings
in unlabeled data. Since the company has no predefined categories, unsupervised clustering is
appropriate. Classification (A) requires labeled data; regression (B) predicts continuous values;
reinforcement learning (D) uses reward-based learning.
, 4
Question 5
A model trained to predict housing prices achieves an R² score of 0.89 on the test set. What
does this indicate?
A. The model correctly classifies 89% of houses
B. The model explains 89% of the variance in housing prices
C. The model has 89% precision in its predictions
D. The model has an error rate of 11%
-” detailed answer 100 % correct :-”B
Rationale: R² (coefficient of determination) measures the proportion of variance in the
dependent variable that is predictable from the independent variables. An R² of 0.89 means
89% of the variance in housing prices is explained by the model.
Question 6
Which of the following is an example of a real-world AI application?
A. Sorting a list of numbers
B. Fraud detection in financial transactions
C. Calculating the sum of two numbers
D. Alphabetizing a list of names
-” detailed answer 100 % correct :-”B
Rationale: Fraud detection is a common AI/ML application that involves learning patterns from
data to identify anomalies. Sorting and arithmetic operations (A, C, D) are deterministic
algorithmic tasks that do not require machine learning.
Question 7
What is the primary difference between batch inference and real-time inference?
A. Batch inference is always more accurate
B. Real-time inference provides immediate predictions; batch inference processes data in bulk
C. Batch inference requires less data
D. Real-time inference is always more expensive
