Amazon AIF-C01 Exam Dumps

Fine-tuning involves training an already pre-trained FM on a smaller, labeled dataset for task specialization.

Data selection is about curating training data.

Pre-training is the initial training phase on massive datasets.

Evaluation happens after training, not during.

Reference:

AWS Documentation -- Fine-tuning in Amazon Bedrock

Amazon CloudWatch is designed for real-time monitoring of applications and infrastructure. It supports metrics and logs for ML model performance and resource utilization. According to the AWS Certified AI Practitioner Study Guide:

''Amazon CloudWatch is a monitoring service that provides data and actionable insights to monitor your ML workloads and applications in real time, ensuring performance and scalability.''

Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model's parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

'Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks.'

(Source: AWS Bedrock User Guide, Model Customization)

Detailed

Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.

Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.

Option C: Model quantizationModel quantization reduces the precision of a model's weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.

Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.

AWS Bedrock User Guide: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html)

AWS AI Practitioner Learning Path: Module on Model Training and Customization

Amazon SageMaker Developer Guide: Fine-Tuning Models (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)

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 havefaster 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 Reference:

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