AWS Certified AI Practitioner (AIF-C01)

AWS AI Practitioner Practice Test

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Questions updated at Jul 10, 2026, 12:01 AM CDT

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AWS AI Practitioner

AWS Certified AI Practitioner (AIF-C01)

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Question 1 of 10

Objective 1.1 Fundamentals of AI and ML

Which statement best differentiates a generative model from a classification model within the context of Amazon Bedrock?

Concept tested: Fundamentals of AI and ML (1.1)

Source:Submit prompts and generate responses with model inference

Question 2 of 10

Objective 2.2 Fundamentals of Generative AI

A legal team requires responses that strictly adhere to a defined policy scope. What prompt change would most effectively ensure the model operates within these constraints?

Concept tested: Fundamentals of Generative AI (2.2)

Question 3 of 10

Objective 1.2 Fundamentals of AI and ML

Which AWS service helps discover and classify sensitive data in Amazon S3 before that data is used by an AI workload?

Concept tested: Fundamentals of AI and ML (1.2)

Question 4 of 10

Objective 2.1 Fundamentals of Generative AI

According to AWS prescriptive guidance, what initial RAG option should a team typically leverage unless custom design requirements necessitate a different approach?

Concept tested: Fundamentals of Generative AI (2.1)

Question 5 of 10

Objective 1.3 Fundamentals of AI and ML

A team is building a real-time fraud detection model. They are performing feature engineering to convert raw transaction data into meaningful signals. Which stage of the ML lifecycle is this activity?

Concept tested: Fundamentals of AI and ML (1.3)

Question 6 of 10

Objective 2.2 Fundamentals of Generative AI

When aiming for more deterministic and less variable responses from an inference model, which parameter should you typically reduce?

Concept tested: Fundamentals of Generative AI (2.2)

Question 7 of 10

Objective 1.1 Fundamentals of AI and ML

When a SageMaker training job runs, which lifecycle step is adjusting model parameters to reduce error on the training data?

Concept tested: Fundamentals of AI and ML (1.1)

Question 8 of 10

Objective 2.1 Fundamentals of Generative AI

An application needs only the relevant source chunks from a knowledge base and will handle answer generation itself. Which Bedrock API should it call?

Concept tested: Fundamentals of Generative AI (2.1)

Question 9 of 10

Objective 1.2 Fundamentals of AI and ML

Which SageMaker feature can automatically build, train, evaluate, and rank model candidates to help a team choose a strong starting model?

Concept tested: Fundamentals of AI and ML (1.2)

Question 10 of 10

Objective 2.1 Fundamentals of Generative AI

Even with a well-crafted prompt, a token-prediction model can still generate inaccurate or misleading information. What is the primary reason for this behavior?

Concept tested: Fundamentals of Generative AI (2.1)

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AIF-C01 AWS AI Practitioner

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UpdatedJul 10, 2026, 12:01 AM CDT

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Question 1 Which statement best differentiates a generative model from a classification model within the context of Amazon Bedrock?

Answer choices

A. A generative model creates new content, while a classifier assigns labels to inputs.
B. A generative model can only run in batch mode, while a classifier runs in real time.
C. A generative model never uses prompts.
D. A classifier always requires reinforcement learning.

Correct answer

A generative model creates new content, while a classifier assigns labels to inputs.

Bedrock inference documentation highlights generation tasks such as text, images, and video, which create new outputs from inputs. A classifier, by contrast, predicts a category or label rather than generating novel content.

Wrong-answer review

B. A generative model can only run in batch mode, while a classifier runs in real time.: The statement about batch mode and real-time operation is inaccurate.
C. A generative model never uses prompts.: Generative models frequently use prompts.
D. A classifier always requires reinforcement learning.: Classifiers do not always require reinforcement learning.

Objective/domain: Fundamentals of AI and ML (1.1)

Source: Submit prompts and generate responses with model inference

Question 2 A legal team requires responses that strictly adhere to a defined policy scope. What prompt change would most effectively ensure the model operates within these constraints?

Answer choices

A. Spell out the policy boundaries and the prohibited response behavior
B. Leave the scope implied and hope the model infers it
C. Raise temperature for more variation
D. Remove all examples and context

Correct answer

Spell out the policy boundaries and the prohibited response behavior

Objective/domain: Fundamentals of Generative AI (2.2)

Source: Design a prompt - Amazon Bedrock

Question 3 Which AWS service helps discover and classify sensitive data in Amazon S3 before that data is used by an AI workload?

Answer choices

A. Amazon GuardDuty
B. Amazon Macie
C. AWS Lambda
D. Amazon SageMaker

Correct answer

Amazon Macie

Objective/domain: Fundamentals of AI and ML (1.2)

Source: Discovering sensitive data with Macie

Question 4 According to AWS prescriptive guidance, what initial RAG option should a team typically leverage unless custom design requirements necessitate a different approach?

Answer choices

A. Cross-Region inference profiles only
B. Knowledge Bases for Amazon Bedrock
C. A self-managed graph database first
D. Manual prompt copy-and-paste only

Correct answer

Knowledge Bases for Amazon Bedrock

Objective/domain: Fundamentals of Generative AI (2.1)

Source: Choosing a Retrieval Augmented Generation option on AWS

Question 5 A team is building a real-time fraud detection model. They are performing feature engineering to convert raw transaction data into meaningful signals. Which stage of the ML lifecycle is this activity?

Answer choices

A. Red-teaming
B. Secrets rotation
C. Feature engineering
D. Inference

Correct answer

Feature engineering

Objective/domain: Fundamentals of AI and ML (1.3)

Source: Feature Processing - Amazon SageMaker AI

Question 6 When aiming for more deterministic and less variable responses from an inference model, which parameter should you typically reduce?

Answer choices

A. Retention period
B. Sampling window in CloudTrail
C. Temperature
D. Request timeout

Correct answer

Temperature

Objective/domain: Fundamentals of Generative AI (2.2)

Source: Influence response generation with inference parameters

Question 7 When a SageMaker training job runs, which lifecycle step is adjusting model parameters to reduce error on the training data?

Answer choices

A. Monitoring
B. Inference
C. Deployment
D. Training

Correct answer

Training

Objective/domain: Fundamentals of AI and ML (1.1)

Source: Model training - Amazon SageMaker AI

Question 8 An application needs only the relevant source chunks from a knowledge base and will handle answer generation itself. Which Bedrock API should it call?

Answer choices

A. Retrieve
B. RetrieveAndGenerate
C. CreateModelCustomizationJob
D. ListPrompts

Correct answer

Retrieve

Objective/domain: Fundamentals of Generative AI (2.1)

Source: Query a knowledge base and retrieve data - Amazon Bedrock

Question 9 Which SageMaker feature can automatically build, train, evaluate, and rank model candidates to help a team choose a strong starting model?

Answer choices

A. Amazon Rekognition
B. Amazon Comprehend
C. AWS Lambda
D. Amazon SageMaker Autopilot

Correct answer

Amazon SageMaker Autopilot

Objective/domain: Fundamentals of AI and ML (1.2)

Source: SageMaker Autopilot - Amazon SageMaker AI

Question 10 Even with a well-crafted prompt, a token-prediction model can still generate inaccurate or misleading information. What is the primary reason for this behavior?

Answer choices

A. Because it is predicting likely continuations unless grounded with relevant context
B. Because Bedrock blocks all factual prompts by default
C. Because CloudTrail removes missing tokens
D. Because KMS changes the output sequence

Correct answer

Because it is predicting likely continuations unless grounded with relevant context

Objective/domain: Fundamentals of Generative AI (2.1)

Source: Prompt engineering concepts - Amazon Bedrock

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