AIF-C01 Exam Dumps - AWS Certified AI Practitioner Exam(AI1-C01)

To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and have faster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.

Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices":This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.

Option B:"Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.

Option C:"Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.

Option D:"Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.

AWS AI Practitioner References:

Optimizing AI Models for Edge Devices on AWS:AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

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.