AWS Certified AI Practitioner (AIF-C01) Practice Exam
Prepare for the AWS Certified AI Practitioner exam with our practice exam simulator. Realistic questions on AI, machine learning and generative AI fundamentals, with detailed explanations, help you find your knowledge gaps and assess your readiness.
Exam domains
The AIF-C01 questions are grouped into 5 domains divided into 14 tasks.
AIF-C01 example questions
One example question per domain from the AIF-C01 question bank, with the correct answer and its explanation. Sign in to the simulator to answer them with all answer options.
A telecommunications company wants to use Amazon SageMaker AI to discover previously unknown groups of customers with similar usage patterns. It has a table with one row per customer and columns for customer_id, account_age_months, average_monthly_data_usage, and average_monthly_call_minutes. No customer groups have been defined or assigned. Which approach best matches the available data and the company's goal?
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CORRECT ANSWERTreat the table as structured, unlabeled data and use the account-age and usage columns as inputs to a clustering model.
The consistent rows and columns make this structured, tabular data. It is unlabeled for customer grouping because no group assignments exist. Clustering in SageMaker AI can discover similarities without predefined group labels.
A logistics company uses an Amazon Bedrock foundation model to assign shipment exception reports to company-specific routing categories. Providing expert-labeled examples in each prompt improves accuracy, but the examples make requests too long. Sending those examples once does not improve later, independent requests. The company has thousands of reviewed report-and-category pairs, and its selected model supports customization. It wants a reusable adaptation that works without including labeled examples in every request. Which approach best meets these requirements?
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CORRECT ANSWERFine-tune the model using the reviewed report-and-category pairs, then use the customized model for classification.
Fine-tuning uses targeted examples to update learned parameters or adaptation parameters. It can teach the report-to-category mapping as reusable behavior without requiring the training examples in every inference request.
A consulting firm uses a retrieval-augmented generation (RAG) assistant to answer questions about market research. Document embeddings are stored in an Amazon OpenSearch Service vector index. The firm switches to a different embedding model for user questions but leaves the existing document embeddings unchanged. Although both embedding models produce vectors of the same length, the assistant now retrieves unrelated passages. Which change should the firm make to restore meaningful retrieval?
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CORRECT ANSWERRegenerate the document embeddings with the new embedding model, rebuild the OpenSearch vector index, and use that model for query embeddings.
Document and query embeddings must use compatible representations for meaningful vector similarity searches. Regenerating the document embeddings with the new model aligns them with query embeddings. Equal vector lengths alone do not establish compatibility.
A healthcare provider is evaluating an AI model that recommends patients for additional screening. The model has high overall accuracy, but the prevalence of the condition differs across demographic groups. Clinical leaders require that patients who actually have the condition have comparable chances of being referred, regardless of demographic group. Which evaluation approach most directly determines whether the model meets this requirement?
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CORRECT ANSWERAmong patients with independently confirmed cases, compare referral rates across demographic groups and investigate any material gaps.
This compares true positive rates across groups, directly testing whether patients who have the condition have comparable chances of referral. Investigating material gaps supports addressing harmful disparities before deployment.
A company is piloting an AI assistant on AWS that summarizes regulatory publications and includes citations to source documents. During user testing, compliance analysts assume that a cited summary has been verified against its sources and can be used without further checking. The company's governance policy requires users to understand the system's limitations before receiving production access. Which change to mandatory onboarding would BEST address the finding?
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CORRECT ANSWERExplain that citations identify supporting material but do not establish correctness, demonstrate how to compare summaries with the original sources, and define when to request expert review.
This provides understandable information about the assistant's limitations and teaches appropriate use. Source verification and an expert-review path directly address the mistaken assumption that citations prove a summary is correct.
Exam mode and practice mode
| Exam mode | Practice mode | |
|---|---|---|
| Questions count | 65 | 10 - 75 |
| Time limit | 90 minutes | Optional, 1 - 210 minutes |
| Exam scope | 5 domains with the official questions ratio | Selected domains with the official questions ratio |
| Correct answers | After exam submission | After exam submission or after each answer |
| Question types | Mix of single and multiple correct answers | Single, multiple or both |
| Question hints | Never | Optional |
| Question domain | Revealed after exam submission | Revealed after submission or during the exam |
| Scoring | 15 of 65 questions do not count towards the result | Official AWS method or mathematical mean |
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