Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 11)

Updated On: 19-Oct-2026

A company is creating a model to label credit card transactions. The company has a large volume of sample transaction data to train the model. Most of the transaction data is unlabeled. The data does not contain confidential information.
The company needs to obtain labeled sample data to fine-tune the model.
Which solutions will meet these requirements? (Choose two.)

  1. Run batch inference jobs on the unlabeled data.
  2. Run an Amazon SageMaker AI training job that uses the PyTorch Distributed library to label data.
  3. Use an Amazon SageMaker Ground Truth labeling job with Amazon Mechanical Turk workers.
  4. Use an optical character recognition model trained on labeled samples to label unlabeled samples.
  5. Run an Amazon SageMaker AI labeling job.

Answer(s): C,E

Explanation:

Amazon SageMaker Ground Truth with Mechanical Turk workers enables human labeling of large unlabeled datasets, and SageMaker AI labeling jobs can automatically generate labels using machine learning workflows,
providing the labeled data needed for fine-tuning.



Which benefit does a company gain by using ongoing pre-training to fine-tune a foundation model (FM)?

  1. Decreased model complexity
  2. Improved model performance over time
  3. Decreased time required to train the model
  4. Reduced model training cost

Answer(s): B

Explanation:

Ongoing pre-training continues training a foundation model on additional relevant data, helping the model improve its knowledge and performance over time for the target domain or use case.



A company wants to build an AI assistant to provide responses to user queries. The AI assistant must evaluate specific data sources, query external APIs, generate response options, and compare and prioritize response options.
Which Amazon Bedrock feature or resource will meet these requirements?

  1. Prompt Management
  2. Response streaming
  3. Knowledge Bases
  4. Agents

Answer(s): D

Explanation:

Amazon Bedrock Agents enable AI assistants to interact with specific data sources, query external APIs,
generate and compare response options, and prioritize results, making them the ideal feature for building advanced, task-oriented chatbots with decision-making and orchestration capabilities.



A company plans to implement a generative AI (GenAI) tool to create content that is based on trending social media topics.
Which option represents a legal risk of creating content by using this tool?

  1. Copyright infringement claims if the GenAI tool produces content too similar to existing training data
  2. Diversity claims for making the content that is generated more diverse and inclusive
  3. Bias in generated content
  4. International trade restrictions on AI technology deployment

Answer(s): A

Explanation:

Generative AI can create content that closely resembles copyrighted material present in its training data. If the generated output is substantially similar to protected works, the company could face copyright infringement claims, making intellectual property risk an important legal consideration.



A financial company has offices in different countries worldwide. The company requires that all API calls between generative AI applications and foundation models (FM) must not travel across the public internet.
Which AWS service should the company use?

  1. AWS PrivateLink
  2. Amazon Q
  3. Amazon CloudFront
  4. AWS CloudTrail

Answer(s): A

Explanation:

AWS PrivateLink provides secure, private connectivity between VPCs and AWS services, ensuring that API
calls do not traverse the public internet. This meets the requirement for private communication between generative AI applications and foundation models across global offices.



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