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IT Specialist Artificial Intelligence INF-307
The exam's own six domains, the podcast episode for each objective, and a timed challenge per domain when you want to grind. This one is not review: one programming course is enough to start, and every episode teaches from the beginning.
- Exam code
- INF-307 Certiport prints no exam code on its own pages. The number 307 is the one in the objective document's own filename, ITS OD 307 Artificial Intel 0826. Read the objective document where precision matters.
- Questions
- 45 to 50
- Time
- 50 minutes
- Passing score
- not published on the Certiport pages read 2026-09-11
The other prep pages
How a level is earned
A challenge draws its questions at random from that domain's bank. Earn the level by getting enough of them right before the time runs out. Levels come in order, one domain at a time.
| Level | Questions | Right to earn it | Time |
|---|---|---|---|
| 1 | 5 | 5 | 10 minutes |
| 2 | 5 | 5 | 5 minutes |
| 3 | 10 | 9 | 5 minutes |
| 4 | 10 | 9 | 2 minutes |
| 5 | 10 | 9 | 1 minute |
This browser stores your level for each domain and your last few attempts. Nothing else. It is practice, not a grade, and no score here reaches anyone. Signing in copies those same levels to your own account so another device can read them, and nothing more: your email address is not stored in this browser, and nobody else can see your levels. Signing out leaves the levels on this browser where they are.
Domain 1: AI Problem Definition
- 1.1 Framing the problemState the problem the AI is meant to solveListen: lessonBonus: ten practice questions
- 1.2 Expertise and resourcesName the expertise and resources the problem needsListen: lessonBonus: ten practice questions
- 1.3 Classifying the problemClassify the problem by learning typeListen: lessonBonus: ten practice questions
- 1.4 Responsible usePlan for responsible useListen: lessonBonus: ten practice questions
- 1.5 Transparency and validationChoose what to disclose and what to validateListen: lessonBonus: ten practice questions
Not started
Domain 2: Data Collection and Transformation
- 2.1 Data sourcesIdentify data sources and what the data must satisfyListen: lessonBonus: ten practice questions
- 2.2 Data qualityJudge whether the data is good enoughListen: lessonBonus: ten practice questions
- 2.3 Data conversionPut the data into a form a model can useListen: lessonBonus: ten practice questions
Not started
Domain 3: Feature Engineering
- 3.1 Feature selectionSelect the features the model will useListen: lessonBonus: ten practice questions
- 3.2 Feature engineeringTransform the featuresListen: lessonBonus: ten practice questions
- 3.3 Train and test splitSplit the data into training and test setsListen: lessonBonus: ten practice questions
- 3.4 Documenting data decisionsWrite down the decisions made about the dataListen: lessonBonus: ten practice questions
Not started
Domain 4: AI Algorithms and Models
- 4.1 Algorithm choiceJudge which algorithms fit the problemListen: lessonBonus: ten practice questions
- 4.2 Training a modelTrain a model with the chosen algorithmListen: lessonBonus: ten practice questions
- 4.3 Evaluating a modelEvaluate accuracy, precision, and sensitivityListen: lessonBonus: ten practice questions
- 4.4 Sources of biasFind where bias could enterListen: lessonBonus: ten practice questions
- 4.5 Model selectionSelect one model after experimentingListen: lessonBonus: ten practice questions
- 4.6 Telling the data storyPresent the result and get approvalListen: lessonBonus: ten practice questions
Not started
Domain 5: Application Integrity and Deployment
- 5.1 Production pipelineDesign the production pipeline and the application it feedsListen: lessonBonus: ten practice questions
- 5.2 Production problemsPlan for what goes wrong in productionListen: lessonBonus: ten practice questions
- 5.3 Security planBuild the security planListen: lessonBonus: ten practice questions
- 5.4 Customer trainingTeach customers what the product does and does not doListen: lessonBonus: ten practice questions
Not started
Domain 6: Maintaining and Monitoring AI in Production
- 6.1 OversightKeep the running system under oversightListen: lessonBonus: ten practice questions
- 6.2 Business impactMeasure the business impactListen: lessonBonus: ten practice questions
- 6.3 Impact on peopleMeasure the impact on people and communitiesListen: lessonBonus: ten practice questions
- 6.4 User feedbackHandle what users tell youListen: lessonBonus: ten practice questions
- 6.5 Periodic reviewReview the whole operation on a scheduleListen: lessonBonus: ten practice questions
Not started
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