# FAST-12 Projects: AI Practitioner Certification Prep (AIF-C01)

<p class="meta">FAST-12 AI Practitioner Certification Prep · 7formations · Instructor: Wadood Chaudhary · All examples fictional · Practice content, not real exam items</p>

This is a certification-prep course, so the projects build exam readiness and a work artifact you can reuse. Use fictional data or a sanitized description of a use case at your job (no customer personal information, PHI, or confidential details; your manager approves the use case). Scoring: Not yet / Meets / Strong. Completion needs Meets on every capstone criterion and 80% or higher on two timed practice exams (one resubmission allowed).

## Capstone: AWS Generative-AI Use-Case Brief

**What you produce:** a 2-page brief recommending how a fictional or sanitized organization should build one generative-AI use case on AWS, plus a 5-minute walkthrough in session 5. The brief exercises every exam domain: fundamentals (Domain 1), generative-AI trade-offs (Domain 2), foundation-model application design (Domain 3), responsible AI (Domain 4), and security and governance (Domain 5).

### Required contents

| Section | Content | Domain |
| --- | --- | --- |
| Problem and fit | Business problem; why AI (or why not); success metric | 1, 2 |
| Approach | Prompting, RAG (knowledge base), fine-tuning, or agent; why; what you rejected and why | 3 |
| Model choice | Two candidate models in Bedrock; comparison on quality (your evaluation set), latency, and cost per month (show the math) | 2, 3 |
| Evaluation | Evaluation set of 10+ questions with expected answers; automatic or LLM-as-a-judge plus human review; pass threshold | 3 |
| Responsible AI | Top three risks, the responsible-AI dimension of each, and the control (guardrail, human review, disclosure) | 4 |
| Security and governance | Shared responsibility split; IAM roles; encryption (KMS); logging (CloudTrail, invocation logs); data classification; compliance needs (for example, HIPAA BAA via AWS Artifact, G.L. c. 93H / 201 CMR 17.00 for MA personal information) | 5 |
| Rollout | Pilot scope, owner, review date, rollback trigger | 3, 5 |

### Rubric

| Criterion | Not yet | Meets | Strong |
| --- | --- | --- | --- |
| Fit and approach | No reason given for the approach, or AI used where a rule would do | Approach justified; one alternative rejected with a reason | Two alternatives compared on cost, effort, and quality |
| Model comparison | No numbers, or cost math wrong | Quality and monthly cost compared with correct math | Latency and context-window limits included; sensitivity to volume shown |
| Evaluation | No evaluation set | 10+ questions with expected answers and a pass threshold | Includes "not in source" and injection tests; human review defined |
| Responsible AI | Risks generic or controls missing | Three specific risks, each mapped to a dimension and control | Residual risk and monitoring plan stated |
| Security and governance | Shared responsibility wrong, or no IAM, encryption, or logging | All five covered correctly for the use case | Least-privilege policy sketched; retention and audit cadence set |
| Walkthrough | Over time or unclear | 5 minutes; recommendation first | Answers two questions using exam vocabulary precisely |

### Example (excerpt): Charles River Family Dental (fictional) booking assistant

> **Problem and fit.** Front desk spends about 9 hours a week answering hours, location, and insurance-network questions. Success: 60% of those questions answered by the assistant with 95% correct answers on the evaluation set. Booking itself stays in the practice system (a rule-based flow, no AI needed).
>
> **Approach.** RAG over the office FAQ and accepted-insurance list using a Bedrock knowledge base. Rejected fine-tuning: the facts change monthly and RAG updates with a re-sync; fine-tuning would need retraining and gives no citations.
>
> **Model choice.** Model A ($0.80/$3.20 per million tokens, sandbox rates) vs Model B ($3/$15). At 200 questions a day, 1,500 input and 150 output tokens each, 30 days: A ≈ $10.08 a month; B ≈ $40.50 a month. Both passed 19 of 20 evaluation questions; choose A.
>
> **Responsible AI.** (1) Clinical advice to patients (safety): Bedrock guardrail denied topic plus a blocked message pointing to the office phone and 911. (2) Patient details repeated in chat (privacy): sensitive-information filter masks phone and email; the assistant never sees appointment records. (3) Spanish-speaking patients get worse answers (fairness): evaluation set includes 5 Spanish questions; pass threshold applies to each language.
>
> **Security and governance.** AWS secures the infrastructure and Bedrock service; the practice owns IAM, guardrail configuration, data, and logging. BAA accepted in AWS Artifact before any PHI is in scope; the assistant is designed so no PHI is needed. Encryption with a KMS key; CloudTrail and invocation logging on, 1-year retention; quarterly review by the office manager.

Check the example's math: 200 × 1,500 = 300,000 input tokens a day; × 30 = 9 M; at $0.80 = $7.20. Output 200 × 150 × 30 = 0.9 M; at $3.20 = $2.88. Total $10.08. Model B: 9 × $3 = $27.00 + 0.9 × $15 = $13.50; total $40.50.

---

## Mini-project 1: Service Map Flashcards (sessions 1-4)

**What you produce:** a one-page service map of the in-scope services you are most likely to confuse, in your own words, plus 20 self-written flashcards (use case → service).

**Requirements:** at least 15 services across AI services (Comprehend, Lex, Polly, Rekognition, Textract, Transcribe, Translate, Personalize), generative-AI services (Bedrock, Bedrock AgentCore, Nova, SageMaker AI, JumpStart, Amazon Quick), and security and governance services (IAM, KMS, CloudTrail, Config, Macie, Artifact, Inspector). Each card has one sentence on **when to use** it and one on **a look-alike** and how to tell them apart.

| Criterion | Not yet | Meets | Strong |
| --- | --- | --- | --- |
| Coverage | Under 15 services, or out-of-scope services | 15+ in-scope services across all three groups | All AI services and all listed security services |
| Accuracy | Any wrong use case | All use cases correct | Look-alike distinctions tested with a partner |
| Own words | Copied descriptions | Own wording, one sentence each | Each card tied to a lab or case |

**Example card:** *Front:* "Pull totals and line items from scanned supplier invoices." *Back:* Amazon Textract. Look-alike: Amazon Comprehend analyzes text you already have (sentiment, entities); Textract gets the text and structure out of documents first.

## Mini-project 2: Teach-Back on One Task Statement (session 5)

**What you produce:** a 3-minute explanation of the task statement you scored lowest on, with one original practice question and its explanation.

| Criterion | Not yet | Meets | Strong |
| --- | --- | --- | --- |
| Accuracy | Factual error | Correct and matches the exam guide wording | Cites the AWS documentation page used |
| Practice question | Ambiguous or answer cued by length | One clear best answer; plausible distractors; explanation of each wrong option | Scenario-based, fictional, and peer-tested |
| Delivery | Over 3 minutes | 3 minutes; one example | Uses a lab result as the example |

**Example (excerpt):** "Task 3.1: choosing between prompting, RAG, and fine-tuning. In Lab 3 our knowledge base missed hearing protection because the PPE table was split across chunks. That's a retrieval problem, so the fix is chunking, not fine-tuning..."