AIF-C01 Exam Dumps - Amazon Web Services AWS Certified AI Practitioner Questions and Answers

Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.

Sampling Bias:

Occurs when the training data is not representative of the broader population, leading to skewed model outputs.

In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.

Why Option B is Correct:

Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.

Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.

Why Other Options are Incorrect:

A. Measurement bias: Involves errors in data collection or measurement, not sampling.

C. Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.

D. Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.

Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.

Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.

Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.

Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.

Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.

AWS AI Practitioner References:

Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

Amazon SageMaker endpoints provide fully managed, serverless model deployment for real-time and batch predictions, allowing companies to deploy ML models without handling any servers or infrastructure management.

D is correct: SageMaker endpoints let you deploy, scale, and monitor ML models with no infrastructure overhead.

A and B require infrastructure management.

C (CloudFront/S3) is not for model deployment, but for static content delivery.

“Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure.”

(Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)

When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.

Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.

Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.

Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.

Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.

AWS AI Practitioner References:

Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.