Amazon Web Services Updated AIF-C01 Exam Questions and Answers by angus

Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create realistic data.

Option A (Correct): " Generative adversarial network (GAN) " : This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on.

Option B: " XGBoost " is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data.

Option C: " Residual neural network " is primarily used for improving the performance of deep networks, not for generating synthetic data.

Option D: " WaveNet " is a model architecture designed for generating raw audio waveforms, not synthetic data in general.

AWS AI Practitioner References:

GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.

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

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): " Number of tokens consumed " : This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: " Temperature value " is incorrect as it affects the randomness of the model ' s output but not the cost directly.

Option C: " Amount of data used to train the LLM " is incorrect because training data size affects training costs, not inference costs.

Option D: " Total training time " is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.