Based on the AIF-C01 exam guideReport an error
Today we stop studying new material and solve a mixed set of questions across all 5 domains, just like the real exam. The real AIF-C01 exam does not ask one domain at a time. A foundations question is followed suddenly by a security question, and then a service-mapping question. Getting comfortable with this "domain jump" is today's goal.
Keep two things in mind while solving.
Right before you start, ask yourself the following four things.
| Item | Value | What it means for pacing |
|---|---|---|
| Total questions | 65 (50 scored + 15 unscored) | Unscored questions are not marked. Solve all of them the same way |
| Time | 90 minutes | About 83 seconds per question on average |
| Passing score | 700 on a scale of 100-1,000 | The line is "know it well enough", not a perfect score |
| No answer | Scored as incorrect | Never leave an answer blank |
| Guessing | No penalty for wrong answers | If you don't know, eliminate options and then choose |
Multiplying the domain weights by the 50 scored questions gives a rough question count. Today's 8 questions are also spread evenly across the five domains.
| Domain | Weight | Of 50 scored questions | Typical question |
|---|---|---|---|
| 1. AI/ML Fundamentals | 20% | About 10 questions | Terminology, learning methods, classification and regression |
| 2. Generative AI Fundamentals | 24% | About 12 questions | Tokens, embeddings, hallucination, inference parameters |
| 3. Applications of Foundation Models | 28% | About 14 questions | Choosing RAG vs. fine-tuning, prompting techniques, evaluation metrics |
| 4. Responsible AI | 14% | About 7 questions | Which principle, which tool |
| 5. Security, Compliance, and Governance | 14% | About 7 questions | Whose responsibility, which service |
| Pass | Time | What to do |
|---|---|---|
| First pass | About 75 minutes | Pick a tentative answer for every question. If you get stuck, flag it and move on |
| Second review | About 10 minutes | Re-solve only the flagged questions |
| Final check | About 5 minutes | Confirm there are no blank answers and that multiple-response questions have the required number of selections |
As soon as you read a question, decide "what kind of question is this?". Once the kind is decided, the criteria to recall are decided too.
| Signal in the passage | Question type | Criterion to recall immediately |
|---|---|---|
| "Which learning method", "which type of task" | Concept distinction (Domain 1) | Are there labels? Is the output a category or a number? |
| "Token", "temperature", "different answer every time" | Generative AI behavior (Domain 2) | Probabilistic generation, direction of the parameter |
| "Which AWS service is most suitable" | Service mapping (Domains 1 and 3) | Verb to service, which of the three layers |
| "Answer from the latest internal documents", "learn the tone" | Choosing customization (Domain 3) | Knowledge means RAG; behavior or style means fine-tuning |
| "Disadvantages a specific group", "why this decision" | Responsible AI (Domain 4) | Which principle is broken, then which tool |
| "Whose responsibility", "restrict access", "audit" | Security and governance (Domain 5) | Shared responsibility model, least privilege, records |
The exam guide says four types of questions can appear. All the practice questions in this note are the first type, but you should know the rules of the other three types before you go in.
| Type | Format | Scoring rule | How to solve |
|---|---|---|---|
| Multiple choice | 1 correct answer, 3 incorrect | Score by getting the one right | Cross out the two obviously wrong options first |
| Multiple response | 2 or more correct answers among 5 or more options | You must choose all the correct answers to score | Judge each option true or false separately, then choose the required number |
| Ordering | Place 3-5 items in the correct order | Both the items and their order must be right to score | Decide the first and last first, then fill in the middle |
| Matching | Connect responses to 3-7 prompts | Every pair must be right to score | Connect the sure pairs first and match the rest among themselves |
Let's follow the solving process on a scenario that is not among the practice questions.
Scenario: A hospital is building a patient information chatbot. The chatbot
must answer only from the care guide documents that the hospital
updates every week, and the hospital wants to avoid the burden of
retraining the model. What is the most suitable approach?
Options: (a) Pretrain a model from scratch
(b) Fine-tune every week
(c) Use RAG to retrieve the care guide documents and provide them as grounding
(d) Raise the temperature
1) Question sentence: "most suitable approach" + two conditions (updated weekly, avoid retraining burden)
2) Identify the type: choosing customization -> "knowledge means RAG; behavior or style means fine-tuning"
3) Eliminate: (a) is the most expensive. It directly contradicts the conditions
(d) only raises diversity and does not supply knowledge
4) The remaining two: (b) retraining weekly = violates the "avoid the burden" condition
(c) just swap the documents and it is reflected immediately = meets both conditions
5) Answer: (c)After solving the 8 questions and checking the explanations, classify each question you got wrong or got right by guessing into one of the three below. The prescription differs by category.
| Miss type | How to tell | Prescription |
|---|---|---|
| Didn't know | The content is new even after reading the explanation | Go back to that week's note and reread that section |
| Confused | You narrowed it to two but the criterion separating them was blurry | Write the difference between the two concepts in one sentence yourself (e.g., "Textract extracts, Comprehend analyzes") |
| Misread | You know the content but missed a condition or a negation | Build the habit of reading the question sentence first and checking qualifiers such as "not" and "most" |
| Today's result | What to do tomorrow |
|---|---|
| All 8 right with reasons, finished within 11 minutes | Only skim Day 5's translation table and list of pitfalls |
| Right, but took longer than 11 minutes | Practice solving mapping questions in 30 seconds. Cover the translation table and say the service name |
| Misses clustered in one domain | Reread that domain's review notes (Day 1-3) |
| Misses scattered across several domains, mostly "misread" | It is a problem of reading order, not knowledge. Practice reading the question sentence first |
If you are ready, begin. Tomorrow is the last day. We will put the exam structure, the keyword → service translation table, and the commonly missed pitfalls on a single page.
Click a choice to reveal the answer and explanation.
Question 1
(Domain: AI/ML Fundamentals) Unlabeled customer transaction data — find naturally-occurring groups/segments. Learning mode?
Question 2
(Domain: Generative AI Fundamentals) Same prompt multiple times → slightly different answers each time. Make output more consistent, deterministic?
Question 3
(Domain: FM Application) Call-center recording files → convert to text, then analyze sentiment in that text. Service combo?
Question 4
(Domain: Responsible AI) Generative AI chatbot outputs factually false info plausibly (hallucination). Most effective mitigation?
Question 5
(Domain: Security·Governance) AWS shared responsibility: S3 stored data encryption enabling and access IAM permissions setting — who's responsible?
Question 6
(Domain: FM Application) Medical team wants to build custom diagnostic-assist model from own medical-image data, directly train, tune, deploy, and post-deploy monitor drift. Most fitting service?
Question 7
(Domain: Generative AI Fundamentals) Showed 2-5 prompt examples following pattern, posed real question — model followed the pattern. Weights unchanged. This technique?
Question 8
(Domain: Responsible AI/Governance) AWS managed AI service's intended use cases, limitations, fairness considerations organized to heighten transparency — document?