DJames617 AIF-C01 / Domain 1 / 20%
Fundamentals of AI and ML
AI/ML terminology, model types, training, inference, and use-case recognition.
Official Task Statements
| Task | What to prove |
|---|---|
| AIF-1.1 | Explain basic AI concepts and terminologies. |
| AIF-1.2 | Identify practical use cases for AI. |
| AIF-1.3 | Describe the AI/ML development lifecycle. |
Concepts You Need to Understand
- AI versus ML versus deep learning, supervised and unsupervised learning, inference, features, labels, datasets, model lifecycle, and business use cases.
AWS services involved
- SageMaker AI
- Comprehend
- Rekognition
- Translate
- Textract
Important configurations
- Identify whether a managed AI service is enough or whether custom model training is required.
Exam Decision Patterns
Least operational overhead
Prefer managed and serverless services when they satisfy the requirement. Exceptions appear when the scenario needs host control, unsupported runtimes, specialized network behavior, or exact migration compatibility.
Highly available
Identify the failure boundary. One instance is not HA. Multiple instances in one AZ help capacity but not AZ failure. Multi-AZ handles regional AZ faults. Multi-Region handles regional events but adds complexity and cost.
Durable
Durability is about preserving data. Use replication, versioning, backups, point-in-time recovery, and tested restore plans. A durable backup does not guarantee a low RTO.
Decouple the application
Use SQS for buffering work, SNS for fanout, EventBridge for event routing, and Step Functions for visible workflow state. Add retries, DLQs, and idempotent consumers.
Least privilege
Prefer roles and temporary credentials, scope actions/resources/conditions, watch explicit denies, and remember that resource policies may also be required.
Common Mistakes
- Calling any automation AI.
- Ignoring data quality and labeling.
Example Architecture
Hands-On Activity
Take a public dataset description and list features, labels, and likely bias risks.
For an AWS-account lab, use one of the linked mini labs and keep cleanup steps visible before you start.
Task-by-Task Study Notes
AIF-1.1 - Explain basic AI concepts and terminologies.
This task statement is asking whether you can turn a scenario into a decision. Start by identifying the workload requirement, the control or service family involved, and the tradeoff AWS is testing in this domain.
- Translate the wording into requirements: security, operations, cost, availability, latency, governance, or data behavior.
- Choose the service or configuration that directly satisfies those requirements with the least unnecessary complexity.
- Reject options that are technically possible but miss the domain goal or increase risk without a requirement.
AIF-1.2 - Identify practical use cases for AI.
This task statement is asking whether you can turn a scenario into a decision. Start by identifying the workload requirement, the control or service family involved, and the tradeoff AWS is testing in this domain.
- Translate the wording into requirements: security, operations, cost, availability, latency, governance, or data behavior.
- Choose the service or configuration that directly satisfies those requirements with the least unnecessary complexity.
- Reject options that are technically possible but miss the domain goal or increase risk without a requirement.
AIF-1.3 - Describe the AI/ML development lifecycle.
This task statement is asking whether you can turn a scenario into a decision. Start by identifying the workload requirement, the control or service family involved, and the tradeoff AWS is testing in this domain.
- Translate the wording into requirements: security, operations, cost, availability, latency, governance, or data behavior.
- Choose the service or configuration that directly satisfies those requirements with the least unnecessary complexity.
- Reject options that are technically possible but miss the domain goal or increase risk without a requirement.
Review Checklist
Sources and Review Metadata
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