CAI-2 AI Practitioner (Marketing): assessment blueprint

Review and limits. The course content has completed cross-model editorial review. The task descriptions and operational pass rule below describe the configured exam. Domain weights, timings and the pass standard remain provisional: no completed job-task analysis or empirical standard-setting study is claimed.

What CAI-2 Marketing shows

A CAI-2 AI Practitioner (Marketing) credential shows that the holder produced a marketer's AI-assisted work to the exam's published rubric in a time-boxed exam. The tasks ask them to research and brief with AI without letting invented facts in, produce on-brand content whose every claim is supported, analyze campaign results without the usual metric traps, manage claims, brand and privacy risk, and brief, check and refine an AI assistant's work in a recorded conversation.

CAI-2 is performance-based and open-AI-by-design: you earn it by doing realistic marketing work with AI in a time-boxed exam, not by recalling facts. It is genuinely failable: completing the course does not earn it, and a pasted answer or a single prompt doesn't meet the minimum for your recorded conversation.

CAI-2 builds on the CAI-1 skills (briefing, verification, data handling). CAI-1 is recommended but not required.

Competency domains and weights

| Domain | Weight | A competent candidate can… | | ----------------------------------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | D1. Research, insight and briefing | 23% | use AI to gather and synthesize research while separating sourced findings from unsourced, outdated or small-sample claims; turn customer voice into insight sized to its evidence; write a campaign brief with a measurable objective, a grounded insight, proof points from approved claims, a feasible channel plan and KPIs with baseline, target and method. | | D2. Content creation and verification | 15% | brief AI for on-brand content and hypothesis-driven variants; inventory every claim in a draft; detect invented statistics, overstated claims, misstated offers and omitted conditions; re-issue compliant content within format limits. | | D3. Campaign measurement and analysis | 30% | use AI to plan analysis while calculations run in a spreadsheet; compute pooled rates and cost per objective conversion correctly; judge channels on the metric that matches the objective; account for capped, saturated and small-sample channels and distorting events; make a single, defensible budget recommendation with specific caveats and a test. | | D4. Brand, claims and privacy risk | 17% | apply substantiation, comparison, environmental-claim and endorsement principles; recognize the risks of generated images and likeness; escalate to legal with targeted questions; keep customer personal data out of unapproved tools; personalize at segment level without sensitive inferences. | | D5. AI collaboration and workflow | 15% | give an assistant the materials and constraints it needs; work in checkable steps; challenge, verify and correct its output; keep decisions human; design repeatable workflows with human gates and records. |

The weights are the exam-level balance, derived from the task rubrics below with the three tasks weighted equally. Each task rubric declares its own criterion weights.

Traceability: tasks → criteria → domains

| Task | Criterion (weight within task) | Domain | | ---------------------------- | ----------------------------------------------------------------------------------- | ------ | | 1. Campaign brief | Objective and KPIs (20), Audience and insight (20), Research reliability (20) | D1 | | | Budget and channel plan (15) | D3 | | | Claims and mandatories (10) | D4 | | | AI-collaboration quality (15) | D5 | | 2. Content and claims review | Detects the planted problems (25), Re-issued content (20) | D2 | | | Evidence (15), False alarms (10), Escalation to legal (15) | D4 | | | AI-collaboration quality (15) | D5 | | 3. Campaign analysis | Calculations (25), Metric judgment (20), Recommendation (20), Caveats and test (10) | D3 | | | Communication (10) | D1 | | | AI-collaboration quality (15) | D5 |

Task types

The course and the exam use the same task families (assessment-as-learning).

| Task type | What you do | How it's scored | Main domains | | ------------------------- | ----------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | ------------ | | AI practice (sandbox) | Do real marketing work with an AI assistant: brief, review, refine, deliver. | Rubric, including the transcript | D1, D2, D5 | | Critique | Find the planted errors, unsupported claims and risks in AI output, and justify each against the materials. | Rubric: one checklist item per planted issue, plus corrections and false-alarm avoidance | D2, D3, D4 | | Scenario | Make a sequence of decisions in a realistic marketing workflow. | Deterministic, from the quality of each choice (course only) | D1–D5 | | Work product | Produce a brief, memo, claims review or workflow design. | Rubric with 4–7 analytic criteria | D1–D5 |

How you earn CAI-2 Marketing

Pass the CAI-2 Marketing exam. Course completion is recommended, and the module checkpoints (80% mastery threshold, unlimited retakes) are the best rehearsal, but they are not required. Experienced marketers can take the exam directly (the challenge path).

The exam

Format: three performance tasks in the browser-based exam workspace, one from each task family:
1. Campaign brief from a research pack. About 55 minutes.
2. Review and re-issue AI-drafted launch content against a fact sheet, claims file and offer terms. About 45 minutes.
3. Budget recommendation from campaign results, with a calculations appendix. About 50 minutes.
Several smaller tasks give a more dependable measure of skill than one large one.
Time: about 2½ hours of work within a single 3-hour time-boxed window. (Provisional; to be confirmed in the beta.)
Task contexts: this exam uses fixed, reviewed task materials and data. Candidates may receive the same company, figures and constraints. Sharing task content is prohibited.
What's graded: your work products for each task, plus your AI-collaboration transcript for that task.
Integrity: honor-code attestation on submission.

Scoring

Each task is scored against a published-format rubric of 4–7 analytic criteria, including an AI-collaboration quality criterion worth 15% of the task. Each criterion is scored 0–4 against behavioral level descriptions, derived from a checklist of observable features.
Every checklist decision must cite verbatim evidence from your submission or your transcript. Claims your work doesn't support earn no credit.
Rubric scoring. Your answers are scored against the rubric's behavioral level descriptions. Before a task variant is used, the workspace assistant's own answer to it is measured and stored for reference. That stored answer does not decide whether a variant is used.
Criterion scores combine into a weighted composite per task (0–4), and the three task composites are averaged.

Pass standard (provisional)

An overall composite of at least 2.5 of 4, with no task below 2.0.
Your recorded conversation with the workspace assistant must also meet a minimum on every task: at least 2.0 of 4 on AI-collaboration quality.
This remains a provisional operational standard. No completed job-task analysis or empirical standard-setting study is claimed. It is reviewed against real submissions after launch.

How decisions are made

AI grading uses three separate scoring passes on a fixed, versioned grading configuration. The passes may share a model.
A person reviews every fail and every flagged attempt before a decision is released. Borderline results and perfect scores get a second check by a different AI Model and go to a person unless both agree. A person also reviews a random sample of clear passes. Every credential decision is logged, and your result shows whether a person confirmed it.
Feedback generated by AI is labeled as AI-generated.
Appeals: you can appeal any result. The first appeal on each attempt is free. While Certavant has one human reviewer, an independent AI Model re-grades your work blind and a person countersigns the outcome. The outcome letter says if that person also confirmed the original decision. Appeals are decided within 5 business days.

What has been checked

Editorial review checks the course and task content for accuracy, alignment, accessibility and bias. It does not establish a validated pass standard.
Scoring checks compare the grading configuration with panel-adjudicated scores on fictional responses to the selected task contexts. They measure agreement with those AI-scored references, not agreement with human experts or coverage of other task variants.
Job-task analysis and standard setting remain provisional. The operational pass rule is not an empirical finding about workplace performance; it is reviewed against real submissions after launch.

AI-use policy for the exam

CAI-2 is open-AI-by-design: using AI well is part of the skill being assessed.

You may:

use the AI assistant provided in the exam workspace, as much as the turn limit allows. The limit is shown at the start of the exam;
use a spreadsheet or calculator, your own notes, the course materials and any reference sources.

How your AI use is assessed:

The workspace assistant is the sanctioned AI channel. Every message you send and every reply is recorded server-side, per task, and attached to your submission as a transcript appendix.
The transcript is graded under AI-collaboration quality: whether you supplied the materials and constraints, worked in steps, checked and challenged the output, corrected its errors, and made the decisions yourself. It is 15% of each task's score, and it has its own minimum on every task, so each task needs a recorded conversation with the assistant that meets it.
Answers are scored against the rubric, and your recorded conversation must meet the minimum on every task. A pasted answer or a single prompt doesn't meet it.

You may not:

use any AI tool other than the workspace assistant during the exam. You confirm this in the attestation when you submit;
have another person do any part of the work, or share task content with anyone;
paste real confidential, client or personal data into the assistant. The tasks provide all the information you need;
include text intended to influence the grader, such as instructions to the AI grader or hidden text, in your submission or your conversation with the assistant. This is an integrity violation. It results in a fail and can lead to a ban and revocation of any credential.

The course tutor is not available during the exam.

The credential

Verifiable and shareable (Open Badges 3.0), with an "Add to LinkedIn profile" option. Displayed as CAI-2 AI Practitioner (Marketing).
Evidence-embedded: the credential can link to the work you submitted, with privacy settings you control (public, employer-link-only, or hidden).
Stamped with an expiry date at issue (12–18 months; the final term is to be confirmed) and renewable by assessment.

Content freshness

Most of the course and exam measure durable skills (D1–D3, D5) that change slowly. Material on generated media, platform labeling and tool terms (part of D4) is flagged as short half-life, reviewed regularly, and changes are published in the course changelog. Rules on advertising claims and data protection differ between markets; the course teaches widely shared principles and the exam materials state everything a task needs.