AIF-C01 Exam Dumps - AWS Certified AI Practitioner Exam

Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.

D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.

A (reducing data size) could worsen bias by removing potentially useful diverse data.

B (consistency with historical results) might reinforce existing biases.

C (separate models) is not scalable and can introduce other fairness issues.

“To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented.”

(Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Amazon SageMaker Clarify is designed to detect and explain bias in ML models and datasets.

AWS Responsible AI guidance recommends Clarify to:

Identify bias in predictions

Analyze feature attribution

Support fairness and ethical AI practices

Why the other options are incorrect:

Rekognition (A) analyzes images, not recommendation bias.

Amazon Q Developer (B) assists developers with code.

Comprehend (C) performs NLP tasks, not bias analysis.

AWS AI document references:

Amazon SageMaker Clarify Documentation

Detecting Bias in AI Systems

Responsible AI on AWS

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Feature engineering focuses on:

Creating new features

Selecting the most relevant existing features

Improving model signal and accuracy

AWS ML best practices identify feature engineering as a key driver of predictive performance.

Why the other options are incorrect:

Visualization (A) helps understanding, not feature creation.

Hyperparameter tuning (B) optimizes models, not features.

Data collection (D) expands datasets but does not engineer features.

AWS AI document references:

Feature Engineering Best Practices

Improving Model Accuracy on AWS

ML Model Development Lifecycle

Comprehensive and Detailed Explanation (AWS AI documents):

AWS Responsible AI best practices emphasize that bias should be detected, measured, mitigated, and monitored throughout the ML lifecycle, especially for sensitive domains such as healthcare. When biased outcomes are observed, AWS guidance recommends addressing bias at the data and model level, not only at the output level.

Using Amazon SageMaker Clarify aligns directly with AWS Responsible AI principles because it is designed to:

Detect and quantify bias in datasets and model predictions across sensitive attributes such as demographic groups

Provide pre-training and post-training bias metrics, allowing practitioners to identify where bias originates

Support data-centric mitigation, including improving dataset balance and representativeness

After identifying bias with SageMaker Clarify, collecting additional balanced training data and retraining the model helps ensure that:

The model learns from a more representative dataset

Disparate treatment recommendations based on demographics are reduced

Fairness is improved while maintaining clinical accuracy

Why the other options are not sufficient or aligned with AWS best practices:

B. Prompt engineering can influence outputs but does not address underlying data or model bias and is not sufficient for regulated, high-risk domains like healthcare.

C. Content filtering removes outputs after generation but does not prevent biased decision-making by the model itself.

D. Separate FM endpoints by demographic group increases the risk of reinforcing bias and violates fairness principles rather than mitigating them.

AWS AI Study Guide References:

AWS Responsible AI principles: Fairness and Governance

Amazon SageMaker Clarify: bias detection and mitigation

AWS best practices for ML in high-risk domains such as healthcare

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Amazon Bedrock supports importing custom foundation models that have been trained or fine-tuned outside of Bedrock, including models customized using Amazon SageMaker AI.

Amazon SageMaker AI provides:

Full control over model training and fine-tuning

Ability to train models using approved internal datasets

Advanced customization beyond prompt-based techniques

After customization in SageMaker, the trained model can be imported into Amazon Bedrock for managed, scalable inference and internal deployment.

Why the other options are incorrect:

A (Guardrails) enforce safety, compliance, and output controls; they do not create or fine-tune models.

B (Amazon Personalize) is a recommendation service, not a foundation model customization tool.

C (Agents) orchestrate tasks and tool usage but do not modify or fine-tune model weights.

AWS AI document references:

Amazon Bedrock Documentation — section on Custom model import

Amazon SageMaker AI Overview — section on model training and fine-tuning

Foundation Models on AWS — customization approaches

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens: Would increase costs, not lower them.

D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

The correct answer is D – Purchase Provisioned Throughput on Amazon Bedrock, which provides guaranteed and predictable model capacity for workloads with consistent or steady request volume. According to AWS Bedrock documentation, Provisioned Throughput is specifically designed for production applications that require reliable, consistent inference performance at a controlled cost. It allows customers to reserve a fixed number of model inference units (MIUs), ensuring low latency and cost savings compared to on-demand pricing when traffic is steady. On-demand throughput (option B) is ideal for unpredictable or sporadic usage, but it becomes more expensive for stable traffic patterns because it charges per token with no discount for steady volume. Hosting the model on EC2 (option A) or Lambda (option C) increases operational overhead, requires model containerization, scaling management, and may not support LLM-level GPU performance efficiently. Bedrock Provisioned Throughput eliminates infrastructure management and provides the most cost-effective solution for stable, predictable workloads.

Referenced AWS Documentation:

Amazon Bedrock Developer Guide – Provisioned Throughput

AWS ML Specialty Study Guide – Cost Optimization for Generative AI