Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 43)

Updated On: 19-Oct-2026

A company is integrating AI into its employee recruitment and hiring solution. The company wants to mitigate bias risks and ensure responsible AI practices while prioritizing equitable hiring decisions.
Which core dimensions of responsible AI should the company consider? (Choose two.)

  1. Fairness
  2. Tolerance
  3. Flexibility
  4. Open source
  5. Transparency

Answer(s): A,E

Explanation:

Fairness - Ensuring equitable outcomes across all demographic groups helps mitigate bias risks in hiring decisions.
Transparency - Providing clear explanations of how the AI makes decisions builds trust and allows auditing for bias.



A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.
Which AWS service meets these requirements?

  1. Amazon CloudWatch
  2. AWS CloudTrail
  3. AWS Trusted Advisor
  4. AWS Config

Answer(s): A

Explanation:

Amazon CloudWatch is a highly scalable AWS service designed for monitoring and observability. It provides real-time monitoring of system metrics, including performance data for ML systems, such as resource utilization
(CPU, memory, etc.), model inference latency, request counts, and errors. CloudWatch enables users to set up alarms, visualize metrics, and automate actions based on performance thresholds, making it ideal for monitoring the performance of ML systems.



An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?

  1. Create a custom model training job in PartyRock on Amazon Bedrock.
  2. Use Amazon SageMaker JumpStart to create a training job.
  3. Use a custom script to run an Amazon SageMaker AI model training job.
  4. Create a Jupyter notebook on an Amazon EC2 instance. Use the notebook to train the model.

Answer(s): B

Explanation:

SageMaker JumpStart provides prebuilt solutions and workflows for fine-tuning open source models with minimal setup. It reduces operational effort compared to custom scripts, EC2 notebooks, or PartyRock, making it the most efficient choice.



Which statement describes a generative AI use case for multimodal models?

  1. Deploy multiple scalable and cost-effective versions of a model.
  2. Process large amounts of data to train multiple models.
  3. Write code in multiple programming languages.
  4. Process different data types, such as images, audio, and video.

Answer(s): D

Explanation:

Multimodal generative AI models are designed to process and reason across multiple data types such as images, audio, video, and text within a single model.



A company wants to use large language models (LLMs) to produce code from natural language code comments.
Which LLM feature meets these requirements?

  1. Text summarization
  2. Text generation
  3. Text completion
  4. Text classification

Answer(s): B

Explanation:

Text generation is the feature of large language models (LLMs) that enables them to produce text based on a given prompt or input. In this scenario, the input would be natural language code comments, and the LLM
would generate code based on those comments. This process involves understanding the context of the comment and generating corresponding code, which is the core functionality of text generation.



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