AI/ML Foundations
20%of exam
GenAI Foundations
24%of exam
Foundation ModelsTokensEmbeddingsPromptsInference
Foundation Model Apps
28%of exam
Responsible AI
14%of exam
FairnessExplainabilityTransparencyHuman ReviewSafety
Security + Governance
14%of exam
IAMEncryptionLoggingComplianceCost Controls
Quick Facts
- Exam
- AIF-C01
- Level
- Foundational
- Questions
- 65 total
- Scored
- 50
- Time
- 90 min
- Pass
- 700/1000
- Cost
- $100 USD
- Validity
- 3 years
Supervised vs Unsupervised
Supervised
- Labeled data
- Known answers
- Classification/regression
Unsupervised
- Unlabeled data
- Find patterns
- Clustering/anomaly
Labels vs patterns
Use-Case Picker
- Known labels→Supervised learning
- Hidden groups→Clustering
- Numeric outcome→Regression
- Future time series→Forecasting
- Next-best item→Recommendation
- Policy rewards→Reinforcement learning
AI Terms
- AI
- Human-like tasks
- ML
- Learns from data
- Deep learning
- Neural-network ML
- Model
- Learned pattern
- Training
- Learn parameters
- Inference
- Generate prediction
- Feature
- Input signal
- Label
- Known answer
Batch vs Real-Time
Batch
- Many records
- Offline timing
- Lower urgency
Real-time
- Single request
- Immediate response
- Latency critical
Bulk vs instant
Learning Types
- Supervised
- Labeled data
- Unsupervised
- Unlabeled patterns
- Reinforcement
- Reward feedback
- Classification
- Predict class
- Regression
- Predict number
- Clustering
- Group similar records
- Forecasting
- Predict future values
- Recommendation
- Suggest next item
ML Lifecycle
- Collect
- Gather data
- Prepare
- Clean data
- Train
- Fit model
- Evaluate
- Measure quality
- Deploy
- Serve model
- Monitor
- Watch drift
- Retrain
- Refresh model
- MLOps
- Repeatable ML operations
GenAI Stack
Prompt, retrieve, guard, evaluate
Prompt directsRAG groundsGuardrails constrainEval proves
RAG vs Fine-Tune
RAG
- Adds knowledge
- Uses retrieval
- Keeps model
Fine-tune
- Changes behavior
- Needs training data
- Updates model
Knowledge vs behavior
Model Pattern Picker
- General task→Prompting
- Private knowledge→RAG
- Style/behavior shift→Fine-tuning
- External actions→Agent
- Safety constraints→Guardrails
- Quality proof→Evaluation
GenAI Terms
- FM
- Broad pretrained model
- LLM
- Text foundation model
- Token
- Text unit
- Context window
- Prompt memory size
- Embedding
- Vector representation
- Vector store
- Embedding search index
- Hallucination
- Unsupported output
- Grounding
- Answer from sources
Temperature vs Top-p
Temperature
- Randomness level
- Higher creative
- Lower deterministic
Top-p
- Token pool
- Probability cutoff
- Nucleus sampling
Randomness vs pool
Prompting
- Zero-shot
- No examples
- Few-shot
- Examples included
- Template
- Reusable prompt
- Role prompt
- Persona/instructions
- Temperature
- Randomness control
- Top-p
- Token pool control
- Stop sequence
- Output cutoff
- Prompt injection
- Instruction attack
Domain Weights
20, 24, 28, 14, 14
Apps largestGenAI secondGovernance equals responsible
Bedrock vs SageMaker
Bedrock
- Managed FMs
- RAG/agents
- No infrastructure
SageMaker AI
- Custom ML
- Train/deploy
- Full lifecycle
FM apps vs ML platform
Service Picker
- Managed FMs→Bedrock
- Custom ML lifecycle→SageMaker AI
- Text sentiment→Comprehend
- Extract documents→Textract
- Image labels→Rekognition
- Speech transcript→Transcribe
Amazon Bedrock
- Bedrock
- Managed FM platform
- Model access
- Enable FMs
- Knowledge Bases
- Managed RAG
- Agents
- Task orchestration
- Action group
- Tool/API bridge
- Guardrails
- Safety controls
- Evaluation
- Model quality checks
- Provisioned throughput
- Reserved capacity
Service Memory
Text, image, document, speech
Comprehend textRekognition imageTextract documentTranscribe speech
FM Patterns
- Prompting
- Instruction only
- RAG
- Retrieve context
- Fine-tuning
- Adapt model behavior
- Pretraining
- Build base model
- Agent
- Plans tool use
- Embedding search
- Semantic retrieval
- Human review
- Manual judgment
- Red team
- Adversarial testing
AWS Services
- SageMaker AI
- ML build/deploy
- Comprehend
- NLP insights
- Rekognition
- Image/video analysis
- Textract
- Document extraction
- Transcribe
- Speech to text
- Translate
- Language translation
- Lex
- Chatbot voice/text
- Polly
- Text to speech
Responsible AI
- Fairness
- Reduce bias
- Explainability
- Understand reasoning
- Transparency
- Disclose AI use
- Privacy
- Protect personal data
- Safety
- Limit harmful output
- Accountability
- Assigned ownership
- Bias
- Skewed outcomes
- A2I
- Human review workflow
Security Memory
IAM, KMS, logs, budgets
IAM accessKMS keysCloudTrail auditBudgets cost
Guardrails vs IAM
Guardrails
- Output safety
- Denied topics
- Content filters
IAM
- Access control
- Principal permissions
- AWS actions
Content vs access
Governance Picker
- Restrict access→IAM
- Encrypt data→KMS
- Audit API calls→CloudTrail
- Monitor metrics→CloudWatch
- Find sensitive data→Macie
- Control spend→Budgets
Security + Cost
- IAM
- Least privilege
- KMS
- Key management
- CloudTrail
- API activity logs
- CloudWatch
- Metrics and alarms
- Config
- Resource compliance
- Macie
- Sensitive data discovery
- Budgets
- Cost alerts
- Cost Explorer
- Spend analysis
Common Traps
Build vs use
AIF is practitioner ≠ Coding out-of-scope
RAG vs training
RAG retrieves ≠ Training changes model
Bedrock vs SageMaker
Bedrock runs FMs ≠ SageMaker builds ML
Guardrails vs security
Guardrails shape content ≠ IAM controls access
Accuracy vs fairness
Accuracy measures correctness ≠ Fairness checks bias
CloudTrail vs CloudWatch
CloudTrail logs API ≠ CloudWatch monitors metrics
Last Minute
- 1.Apps domain largest: 28%
- 2.50 scored, 15 unscored
- 3.Pass = 700 scaled
- 4.Bedrock = managed FMs
- 5.SageMaker AI = ML lifecycle
- 6.RAG grounds with retrieval
- 7.Fine-tune changes behavior
- 8.Guardrails constrain content
- 9.IAM controls AWS access
- 10.Budgets alert on spend
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