Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner Questions 186–190 (page: 39)

Updated On: 1-Oct-2026

A company plans to use a dataset to train a new AI model. The company needs to determine whether a bias against a specific user group exists in the dataset before the company trains the model.
Which solution will meet these requirement?

  1. Use Amazon SageMaker Clarify to compute pre-training bias metrics.
  2. Use Amazon SageMaker Model Cards to compute pre-training bias metrics.
  3. Use Amazon SageMaker Clarify to compute post-training bias metrics.
  4. Use Amazon SageMaker Model Cards to compute post-training bias metrics.

Answer(s): A

Explanation:

Amazon SageMaker Clarify can analyze a dataset before training and compute pre-training bias metrics. This helps identify whether the training data contains bias against specific groups before the model is built.



A company uses an Amazon Bedrock large language model (LLM) in an application. During testing, the company observes different outputs from the same input.
What is the MOST likely cause of this issue?

  1. The LLM is acting in a nondeterministic way.
  2. The guardrails of the LLM are not configured properly.
  3. The LLM has security vulnerabilities.
  4. The LLM is acting in a deterministic way.

Answer(s): A

Explanation:

LLMs often generate outputs nondeterministically, meaning the same input can produce different responses due to inherent randomness in the generation process.



A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

  1. Decrease the temperature value.
  2. Increase the temperature value.
  3. Decrease the length of output tokens.
  4. Increase the maximum generation length.

Answer(s): A

Explanation:

Decreasing the temperature value makes the model's output more deterministic and consistent by reducing randomness in response generation. The other adjustments do not directly ensure consistent responses.



A company is building a generative AI (GenAI) application. The company wants to implement mechanisms to monitor and direct AI system behavior.
Which responsible AI dimension is the company applying?

  1. Fairness
  2. Explainability
  3. Controllability
  4. Safety

Answer(s): C

Explanation:

Controllability is the responsible AI dimension focused on mechanisms that monitor, guide, and constrain an AI
system’s behavior so it operates according to intended policies and requirements.



A company has developed an AI assistant application for customers by using Amazon Bedrock Agents. The company notices that the agents require additional context to make informed decisions consistently. The agents are not accessing external services to complete customer travel booking requests.
The company needs to ensure that the agents access additional context when necessary.
Which solution will meet this requirement?

  1. Negative prompting
  2. Model Context Protocol (MCP)
  3. Zero-shot prompting
  4. Custom API to provide more context

Answer(s): D

Explanation:

Providing a custom API allows the agents to access external data and services, supplying the additional context needed to make informed and consistent decisions for customer travel bookings.



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