AIF-C01
Free AWS Certified AI Practitioner (AIF-C01) Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
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A healthcare company needs to analyze patient X-ray images to detect potential fractures. The system must learn from thousands of labeled X-ray images showing fractured and non-fractured bones, and then classify new images accordingly. Which type of machine learning approach is MOST appropriate for this requirement?
Supervised learning with classification is correct because the company has labeled training data (X-rays marked as fractured/non-fractured) and needs to predict categorical outcomes (fracture detected: yes/no). Classification algorithms learn from labeled examples to categorize new inputs.
A healthcare company wants to generate synthetic patient data for training purposes while ensuring the generated data maintains the statistical properties of the original dataset but doesn't expose actual patient information. Which generative AI approach is MOST appropriate for this requirement?
Variational autoencoders (VAEs) with differential privacy are specifically designed for generating synthetic data that preserves statistical properties while providing mathematical privacy guarantees. Differential privacy adds noise during training to prevent memorization of individual records, making it the best choice for sensitive healthcare data.
A healthcare company needs to extract patient information from thousands of handwritten medical forms daily. The forms contain structured fields (name, date of birth) and unstructured notes. The solution must achieve 95%+ accuracy and handle various handwriting styles. Which AWS AI service BEST meets these requirements?
Amazon Textract is specifically designed for document analysis and OCR with specialized features for forms (key-value pairs) and handwriting recognition. The Queries feature allows extraction of specific fields, and it maintains 95%+ accuracy for handwritten content.
A healthcare company is deploying an AI system to assist doctors in diagnosing rare diseases from medical images. The system will be used across multiple hospitals with diverse patient populations. During testing, the model shows 95% accuracy on the development dataset but only 78% accuracy on images from underrepresented demographic groups. What is the MOST responsible approach to address this issue before deployment?
Collecting additional representative training data and validating performance across all demographic segments directly addresses the fairness issue at its root cause. This ensures the AI system performs equitably across all patient populations before deployment, which is a core principle of responsible AI - particularly fairness and inclusiveness.
A healthcare company is deploying an AI-powered diagnostic assistant on Amazon Bedrock that processes patient medical records. The company must ensure that all prompts and model responses containing Protected Health Information (PHI) are encrypted both in transit and at rest, with full audit trails of all access. The solution must comply with HIPAA requirements while minimizing operational overhead. Which combination of AWS services BEST meets these requirements?
This solution meets all HIPAA requirements: customer-managed KMS keys provide control over encryption for data at rest, VPC endpoints ensure private connectivity without internet exposure (encryption in transit via TLS), and CloudTrail provides comprehensive API audit logs required for HIPAA compliance. This is the most complete solution with minimal operational overhead.
A retail company wants to group customers into segments based on purchasing behavior without predefined categories. They have transaction history data but no labeled customer segments. Which ML approach should they use?
Unsupervised learning with clustering is correct because there are no predefined labels or categories. Clustering algorithms like K-means discover natural groupings in unlabeled data based on similarity patterns in purchasing behavior.
A research team is comparing different foundation models for their document summarization project. They observe that Model A with 70B parameters performs better on their task than Model B with 175B parameters. What is the MOST likely explanation for this outcome?
Task-specific fine-tuning or optimization is the most common reason a smaller model outperforms a larger general-purpose model. A 70B model fine-tuned on summarization tasks will typically outperform a larger general-purpose model that hasn't been optimized for that specific task. This demonstrates that model size alone doesn't determine performance.
A financial services firm wants to implement a chatbot that answers customer questions about investment products. The chatbot must reference the company's proprietary investment guides (500+ PDF documents) and provide accurate, source-cited responses. The firm wants to use foundation models without fine-tuning. Which approach BEST meets these requirements?
RAG with Bedrock Knowledge Bases is designed for this exact scenario: grounding foundation model responses in proprietary documents without fine-tuning. It automatically handles document ingestion, vectorization, retrieval, and source citation.
An e-commerce company uses an AI-powered recommendation system that suggests products to customers. Users have reported that the system seems to reinforce stereotypes, such as showing baby products predominantly to women and power tools predominantly to men. Which action BEST aligns with AWS responsible AI principles?
Implementing fairness metrics allows you to quantitatively measure bias, and adjusting the algorithm to reduce stereotyping while maintaining relevance addresses the root cause. This approach balances fairness with system effectiveness, which is a key responsible AI practice.
A financial services company uses Amazon Bedrock to generate investment recommendations. Regulatory requirements mandate that the company must be able to explain how each AI-generated recommendation was produced and identify which training data influenced specific outputs. The company needs to implement a solution that provides explainability and traceability. Which approach BEST addresses these requirements?
RAG with Amazon Kendra provides direct traceability to source documents, allowing the company to show exactly which data influenced each recommendation. Provenance tracking links outputs to specific knowledge base articles, and Bedrock Agents with detailed logging captures the reasoning chain. This combination directly addresses explainability and traceability requirements for regulatory compliance.
What is the PRIMARY difference between AI, machine learning, and deep learning?
This correctly describes the hierarchical relationship: AI is the broadest concept (any system exhibiting intelligent behavior), ML is a subset of AI (systems that learn from data), and deep learning is a subset of ML (using multi-layered neural networks for representation learning).
What is the primary purpose of the temperature parameter in generative AI model inference?
Temperature controls the randomness of predictions by scaling the logits before applying softmax. Lower temperatures (e.g., 0.1) make the model more deterministic and focused on high-probability tokens. Higher temperatures (e.g., 1.0 or above) increase randomness and creativity by flattening the probability distribution. Temperature 0 = deterministic, always picks highest probability token.
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How much does the AIF-C01 exam cost?
The exam fee is approximately $100 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 5 domains: Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), Security, Compliance, and Governance for AI Solutions (14%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $17.99 (list $54.99) through August 22 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.