AIF-C01PRACTICE QUESTIONS

AIF-C01 practice questions with answers

5 example questions from the AWS Certified AI Practitioner question bank, grouped by exam domain. Pick an answer and check it to see the explanation.

Fundamentals of AI and ML

Question 1Select 1

null Explain basic AI concepts and terminologies

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?

A. Treat average_monthly_data_usage as an existing target label and train a regression model using the remaining columns to produce the customer groups.

B. Treat the table as unstructured, unlabeled data and convert each row into a text document before using a text-clustering model.

C. Treat the table as structured, unlabeled data and use the account-age and usage columns as inputs to a clustering model.

D. Treat the table as labeled data, use customer_id as the target label, and train a supervised model to predict each customer's identifier.

Fundamentals of GenAI

Question 1Select 1

null Explain the basic concepts of generative AI (GenAI)

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?

A. Send the reviewed examples to the original model as ordinary inference requests before each day's classification workload.

B. Continue pre-training the model on unlabeled shipment reports without including the approved routing categories.

C. Reduce the original model's temperature and remove the labeled examples from classification prompts.

D. Fine-tune the model using the reviewed report-and-category pairs, then use the customized model for classification.

Applications of Foundation Models

Question 1Select 1

null Describe design considerations for applications that use foundation models (FMs)

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?

A. Increase the number of passages retrieved from the existing OpenSearch index without changing the document embeddings.

B. Fine-tune the answer-generation model on the research documents while leaving the retrieval configuration unchanged.

C. Increase the generation model's context window so it can interpret the passages retrieved by the existing index.

D. Regenerate the document embeddings with the new embedding model, rebuild the OpenSearch vector index, and use that model for query embeddings.

Guidelines for Responsible AI

Question 1Select 1

null Explain the development of AI systems that are responsible

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?

A. Among patients with independently confirmed cases, compare referral rates across demographic groups and investigate any material gaps.

B. Exclude demographic attributes from the model inputs and verify that inference requests contain none of those attributes.

C. Compare the percentage of all patients referred in each demographic group and require those percentages to be equal.

D. Measure overall accuracy on a larger representative test dataset and approve the model if accuracy exceeds the clinical target.

Security, Compliance, and Governance for AI Solutions

Question 1Select 1

null Recognize governance and compliance regulations for AI systems

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?

A. Teach analysts how the assistant ranks retrieved documents and display relevance scores beside citations so analysts can select the highest-ranked source.

B. Train analysts on approved data classifications, prohibited prompt content, and the retention policy for questions submitted to the assistant.

C. Present the assistant's overall evaluation results and teach analysts to use its average accuracy score when deciding whether an individual cited summary needs review.

D. Explain 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.

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