Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 19)

Updated On: 19-Oct-2026

A company is defining its AI strategy to align with company goals. The company wants to ensure that AI
solutions are developed, deployed, and used in a way that promotes transparency and avoids bias.
Which governance perspective describes this scenario?

  1. Responsible use of AI
  2. Risk management
  3. Data curation
  4. Cloud financial management

Answer(s): A

Explanation:

Responsible use of AI focuses on developing, deploying, and operating AI systems in ways that promote fairness, transparency, accountability, and appropriate treatment of users. This governance perspective directly addresses the company’s goal of reducing bias and ensuring transparent AI practices.



A company is building a chatbot to improve user experience. The company is using a large language model
(LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?

  1. Pairs of chatbot responses and correct user intents
  2. Pairs of user messages and correct chatbot responses
  3. Pairs of user messages and correct user intents
  4. Pairs of user intents and correct chatbot responses

Answer(s): C

Explanation:

Few-shot learning involves providing the model with a few examples to help it understand how to perform the task. For intent detection, the company needs pairs of user messages and the correct user intents, which will help the LLM improve its accuracy in detecting user intents. The other options do not provide the necessary pairing for improving intent detection.



Which scenario represents a practical use case for generative AI?

  1. Using an ML model to forecast product demand
  2. Employing a chatbot to provide human-like responses to customer queries in real time
  3. Using an analytics dashboard to track website traffic and user behavior
  4. Implementing a rule-based recommendation engine to suggest products to customers

Answer(s): B

Explanation:

A generative AI use case involves generating new content, such as natural language responses. A chatbot that provides human-like responses to customer queries uses generative AI models (like large language models) to dynamically create relevant answers, making this a practical example.



A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?

  1. Use a deep learning neural network to perform speech recognition.
  2. Build ML models to search for patterns in numeric data.
  3. Use generative AI summarization to generate human-like text.
  4. Build custom models for image classification and recognition.

Answer(s): A

Explanation:

To meet the requirements of enabling the app to "hear what users say and provide voice responses," the solution must include speech recognition and text-to-speech capabilities. Using a deep learning neural network for speech recognition allows the app to convert spoken words into text. Once the input is understood,
text-to-speech systems can provide voice responses back to users. This approach is fundamental in applications that assist users with visual impairments by enabling interaction through spoken language.



A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices.
Which practice should the company apply when collecting data to decrease model bias?

  1. Use data from only customers who match the demographics of the company's overall customer base.
  2. Collect data from customers who have a past purchase history.
  3. Ensure that the data is balanced and collected from a diverse group.
  4. Ensure that the data is from a publicly available dataset.

Answer(s): C

Explanation:

By gathering training data that reflects the full spectrum of your customer population - across demographics,
behaviors, and preferences - you reduce skew toward any one subgroup and help the recommender treat all users equitably.



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