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AWS Certified AI Practitioner AIF-C01 — AWS practice for the AWS Certified AI Practitioner AIF-C01 credential

Last reviewed 2026-10-26 · CoStudy · how we write and review questions

AWS Certified AI Practitioner AIF-C01 is the AWS Certified AI Practitioner AIF-C01 credential. AWS writes the outline; a fail is another sitting fee and a delayed start, not a school-curve disappointment. Related CoStudy banks (aws_cloud_practitioner, aws_solutions_architect_assoc, aws_developer_assoc, aws_sysops_assoc) test different sittings — using one as a stand-in for AWS Certified AI Practitioner AIF-C01 is how people fail the appointment they paid for.

CoStudy’s AWS Certified AI Practitioner (AIF-C01) bank holds 150 multiple-choice questions, 120 flashcards and 10 scenario simulations. Chapters in this bank follow that outline: Fundamentals of AI and ML; Fundamentals of Generative AI; Applications of Foundation Models; Guidelines for Responsible AI; Security, Compliance, and Governance for AI Solutions. The published material this bank is written against begins: AWS Certified AI Practitioner (AIF-C01) Exam Guide (public, aws.amazon.com) — 5 content domains: 1) Fundamentals of AI and ML (20%) — basic terminology, types, lifecycle, 2) Fundamentals of Generative AI (24%) — foundation models, prompt engineering, RAG, 3) Applications of… Every multiple-choice item on this page shows the full rationale in the HTML. CoStudy is not AWS. We do not have live items, and we do not guarantee a result.

  • 280 total items
  • 150 multiple choice
  • 120 flashcards
  • 10 simulations
  • 5 chapters

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Exam facts

Copied from the cited official page on the date shown. Rows marked unverified were not confirmed on that pass — check the certifying body before you pay or schedule. CoStudy is not the exam owner.

FactDetailSource
What it isAWS Certified AI Practitioner (AIF-C01) is administered under AWS. CoStudy is independent practice, not the exam owner.the AWS Certification exam guides · as of 2026-10
Published outlineAWS Certified AI Practitioner (AIF-C01) Exam Guide (public, aws.amazon.com) — 5 content domains: 1) Fundamentals of AI and ML (20%) — basic terminology, types, lifecycle, 2) Fundamentals of Generative AI (24%) — foundation models, prompt engineering, RAG, 3) Applications of Foundation Models (28%) — Bedrock, SageMaker, application design patterns, 4) Guidelines for Responsible AI (14%) — bias, fairness…the AWS Certification exam guides · as of 2026-10
Domain weights (from the outline blurb)Fundamentals of AI and ML 20%; Fundamentals of Generative AI 24%; Applications of Foundation Models 28%; Guidelines for Responsible AI 14%; Governance for AI Solutions 14%the AWS Certification exam guides · as of 2026-10
Passing score / pass rateOnly the certifying body publishes a cut score or pass rate. This page does not invent one. (unverified)the AWS Certification exam guides · as of 2026-10
Fees and eligibilityApplication, sitting, and retake rules live on AWS’s site and can change. Confirm there before you pay.the AWS Certification exam guides · as of 2026-10

Exam format, sections and weights

Published domain weights from the cited official outline. Re-check the certifying body before you sit — weights move.

SectionPublished weight
Fundamentals of AI and ML20%
Fundamentals of Generative AI24%
Applications of Foundation Models28%
Guidelines for Responsible AI14%
Governance for AI Solutions14%

AWS Certified AI Practitioner AIF-C01 bank mix (this question bank)

Chapter counts below are CoStudy’s mix so you can see where practice time goes. They are not a claim that the live exam uses these item counts.

ChapterItems in this bank
Fundamentals of AI and ML37 (25%)
Fundamentals of Generative AI40 (27%)
Applications of Foundation Models41 (27%)
Guidelines for Responsible AI18 (12%)
Security, Compliance, and Governance for AI Solutions14 (9%)

How this bank is different

  • AWS Certified AI Practitioner AIF-C01 is one AWS exam code. A Cloud Practitioner stem is not an Architect stem.
  • The credited answer is the service that meets the constraint (RPO, encryption, cost, ops), not the logo you used at work.
  • Recertification and exam versions are AWS’s. Pin the code on your voucher before you drill.

Exam format and blueprint

AWS Certified AI Practitioner (AIF-C01) Exam Guide (public, aws.amazon.com) — 5 content domains: 1) Fundamentals of AI and ML (20%) — basic terminology, types, lifecycle, 2) Fundamentals of Generative AI (24%) — foundation models, prompt engineering, RAG, 3) Applications of Foundation Models (28%) — Bedrock, SageMaker, application design patterns, 4) Guidelines for Responsible AI (14%) — bias, fairness, transparency, safety, 5) Security, Compliance, and Governance for AI Solutions (14%). MCQs reference public AWS docs and AI/ML foundations.

What the AWS Certified AI Practitioner AIF-C01 bank covers

Each chapter follows a domain of the published exam outline. Practise one on its own (chapter URLs are in the app and on this page for navigation; they are not sitemap targets):

Study timeline

Pin the exam code first. Then spend the largest block of time on fundamentals of ai and ml (20% in the published blurb). Pair every miss with a diagram or a console click.

Six-week AWS Certified AI Practitioner AIF-C01 block — 10–14 hours/week

Weeks 1–4: walk the chapters in outline order, with a mixed set twice a week. Week 5: only the two weakest chapters. Week 6: two timed sittings in the shape the certifying body describes, then stop adding new outline. AWS Certified AI Practitioner (AIF-C01) samples on this page are for judging rationale quality, not for a full mock.

Twelve-week working-hours plan — 6–8 hours/week

Two or three evenings and one weekend block. Keep a written error log keyed to chapters, not to “I was tired.” The last fourteen days are timing and weak-chapter repair only. If you are also sitting a related exam (aws_cloud_practitioner), do not merge the decks.

Free AWS Certified AI Practitioner (AIF-C01) practice questions

A sample of 12 multiple-choice questions from the bank, with the full rationale shown.

Fundamentals of AI and ML

What is bias in a machine learning model?

  1. Random noise added to inputs during training
  2. The model's inability to fit non-linear data
  3. Systematic error that unfairly favors certain outcomes
  4. The variance of predictions across training runs
  5. The regularization penalty applied to model weights

Answer: C — Systematic error that unfairly favors certain outcomes

A) Data augmentation, not bias. B) Underfitting from limited capacity. C) Correct — systematic skew (statistical or societal) toward certain outcomes. D) Variance, not bias. E) L1/L2 regularization.

Which BEST describes Amazon SageMaker?

  1. A managed service for hosting relational databases
  2. A managed service for foundation model chat only
  3. A managed service for building serverless web APIs
  4. A managed service for end-to-end ML development
  5. A managed service for extract-transform-load pipelines

Answer: D — A managed service for end-to-end ML development

A) RDS. B) That's Bedrock. C) API Gateway/Lambda. D) Correct — build, train, tune, deploy, and monitor ML models. E) AWS Glue.

Which BEST describes the difference between training and inference?

  1. Training fits weights on data; inference uses the trained model to predict
  2. Training predicts on new data; inference updates weights in place
  3. Both stages update weights, only the compute type differs
  4. Both stages only run on CPUs, never on GPUs or accelerators

Answer: A — Training fits weights on data; inference uses the trained model to predict

A) Correct — canonical distinction. B) Reversed. C) Inference is weight-frozen. D) Accelerators are common at both stages.

Fundamentals of Generative AI

Prompt engineering refers to:

  1. Fine-tuning weights on a supervised dataset
  2. Compressing model artifacts for edge deployment
  3. Designing inputs to steer the model's outputs
  4. Auto-scaling the endpoint based on token throughput

Answer: C — Designing inputs to steer the model's outputs

A) Fine-tuning. B) Model compression. C) Correct — role, context, examples, format, and constraints. D) Auto-scaling policy.

Which BEST describes a foundation model?

  1. A small classifier tuned to one specific task
  2. A rules-based knowledge graph used for reasoning
  3. A pretrained deep model adaptable to many tasks
  4. A statistical technique for regression on tabular data
  5. A cloud service that hosts container-based workloads

Answer: C — A pretrained deep model adaptable to many tasks

A) Foundation models are broad, not task-specific. B) Describes symbolic AI. C) Correct — trained on broad data, adapted (fine-tune/prompt/RAG) to many downstream tasks. D) Describes classical stats. E) Describes ECS/EKS/Fargate.

Which BEST describes prompt engineering?

  1. Continuing pre-training on general web text
  2. Updating the transformer's weight matrices offline
  3. Increasing the model's context window at runtime
  4. Redesigning the tokenizer to shrink vocabulary size
  5. Designing inputs, examples, and constraints to guide outputs

Answer: E — Designing inputs, examples, and constraints to guide outputs

A) Continued pre-training. B) Fine-tuning. C) Not a technique — the window is architectural. D) Tokenizer change. E) Correct — role, context, examples, format, constraints.

Applications of Foundation Models

Amazon Rekognition is BEST described as:

  1. A managed relational database service
  2. A managed vision service for images and video
  3. A managed voice synthesis service
  4. A managed conversational chatbot builder

Answer: B — A managed vision service for images and video

A) RDS. B) Correct — labels, faces, moderation, PPE, text-in-image. C) Amazon Polly. D) Amazon Lex.

Which SageMaker feature offers pre-built foundation models and solution templates?

  1. SageMaker JumpStart with model templates
  2. SageMaker Feature Store with online lookups
  3. SageMaker Data Wrangler for data prep
  4. SageMaker Model Monitor for drift alerts

Answer: A — SageMaker JumpStart with model templates

A) Correct — JumpStart is the model + solution catalog you can deploy in a few clicks. B) Feature Store manages ML features. C) Data prep. D) Endpoint monitoring.

Guidelines for Responsible AI

Which AWS capability detects bias in datasets and ML models?

  1. Amazon Macie PII discovery
  2. AWS Config compliance rules
  3. Amazon CloudWatch metrics
  4. SageMaker Clarify bias reports
  5. AWS CloudTrail audit trail

Answer: D — SageMaker Clarify bias reports

A) PII discovery in S3. B) Resource-config compliance. C) Metrics/logs monitoring. D) Correct — bias detection + explainability (SHAP) for ML. E) Account-level API auditing.

Bias in ML systems can arise from which source?

  1. The choice of programming language
  2. Historical bias baked into training data
  3. The AWS region hosting the training job
  4. The number of parallel training instances

Answer: B — Historical bias baked into training data

A) Language-agnostic. B) Correct — biased labels/sampling propagate to the model. C) Region choice is unrelated. D) Compute count is unrelated.

Security, Compliance, and Governance for AI Solutions

A team wants Bedrock invocation logs (prompt + completion) delivered to S3 for audit. What fits BEST?

  1. Enable Bedrock model-invocation logging with an S3 destination
  2. Rely solely on CloudTrail management events for the prompts
  3. Enable VPC Flow Logs on the Bedrock endpoint interface
  4. Turn on GuardDuty ML anomaly detection for the account

Answer: A — Enable Bedrock model-invocation logging with an S3 destination

A) Correct — Bedrock model-invocation logging captures prompts + completions to S3 / CloudWatch. B) CloudTrail logs API calls, not payloads. C) Flow logs capture network metadata, not payloads. D) GuardDuty finds threats, not audit trails.

Where can you obtain AWS audit reports (SOC, PCI, ISO) for compliance reviews?

  1. AWS CloudTrail event history
  2. Amazon CloudWatch Logs Insights
  3. AWS Trusted Advisor cost checks
  4. AWS Artifact compliance portal

Answer: D — AWS Artifact compliance portal

A) API audit trail, not audit reports. B) Application logs. C) Cost/security best-practice checks. D) Correct — Artifact is the self-serve portal for AWS audit reports (SOC, PCI, ISO) and customer agreements.

AWS Certified AI Practitioner (AIF-C01) flashcards

6 sample cards from the 120 in the bank.

Amazon Bedrock?

Managed service for foundation models. Access multiple models via single API.

What is AWS AI Practitioner (AIF-C01)?

Foundational cert (no prerequisites). Tests AI/ML fundamentals + AWS generative AI services.

Pillars of responsible AI?

Fairness, explainability, privacy, security, safety, controllability, governance, transparency, robustness.

Validation vs test data?

Validation: tune hyperparameters during training. Test: final evaluation, untouched until end.

Bedrock model evaluation?

Compare models on your data. Built-in or custom metrics.

Encryption for AI workloads?

Encrypt data at rest (S3, EBS) and in transit. KMS keys for AI services.

Practise the full AWS Certified AI Practitioner (AIF-C01) bank

These samples are a small slice. The full bank runs flashcards, multiple choice and timed mock exams with per-chapter progress tracking, on the web and in the iOS app.

Open AWS Certified AI Practitioner (AIF-C01) →

Frequently asked

What is the AWS Certified AI Practitioner AIF-C01 exam?

AWS Certified AI Practitioner (AIF-C01) Exam Guide (public, aws.amazon.com) — 5 content domains: 1) Fundamentals of AI and ML (20%) — basic terminology, types, lifecycle, 2) Fundamentals of Generative AI (24%) — foundation models, prompt engineering, RAG, 3) Applications of Foundation Models (28%) — Bedrock, SageMaker, application design patterns, 4) Guidelines for Responsible AI (14%) — bias, fairness, transparency, safety, 5) Security, Compliance, and Governance for AI Solutions (14%). MCQs reference public AWS docs and AI/ML foundations.

How is CoStudy’s AWS Certified AI Practitioner AIF-C01 bank organised?

It holds 280 items across 5 chapters: Fundamentals of AI and ML; Fundamentals of Generative AI; Applications of Foundation Models; Guidelines for Responsible AI; Security, Compliance, and Governance for AI Solutions. Free samples on this page include the full rationale, with no account. Chapter counts in the bank are our mix; they are not a promise that the live exam uses the same item counts.

Should I study AWS Certified AI Practitioner AIF-C01 and aws cloud practitioner as one pile?

No. AWS Certified AI Practitioner AIF-C01 is its own sitting. Related banks on CoStudy (aws_cloud_practitioner, aws_solutions_architect_assoc, aws_developer_assoc, aws_sysops_assoc, aws_solutions_architect_pro) exist because candidates confuse neighbouring credentials. Use the outline for the exam on your voucher.

Where do AWS Certified AI Practitioner AIF-C01 fees, cut scores, and pass rates live?

On AWS’s published pages — start at the AWS Certification exam guides. This page quotes a number only when we retrieved it from an official URL, and otherwise says unverified. Prep blogs that tabulate every exam’s price are often stale.

Are these real AWS Certified AI Practitioner AIF-C01 exam questions?

No. CoStudy does not have live items. The samples below are original practice written to the published outline, with rationales. Anyone selling “real exam dumps” is describing something you should not buy.

Primary source

This bank is written against AWS's published exam material. Check the AWS Certification exam guides for the current outline, fees and eligibility rules — those change, and the certifying body is the only authority on them. CoStudy is not affiliated with AWS.

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