Amazon AIF-C01 Exam Dumps

Correct Answer: A

Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models

improve their understanding of textual information by representing concepts in a continuous vector space. Option A (Correct): "Embeddings":This is the correct term, as embeddings provide a way for models to learn relationships between

different objects in their input space, improving their understanding and processing capabilities. Option B:"Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.

Option C:"Models" are the algorithms that use embeddings and other inputs, not the representations themselves.

Option D:"Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.

AWS AI Practitioner References:

Understanding Embeddings in AI and NLP:AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.

Correct Answer: A

Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with

large input data sizes and substantial processing times.

Option A (Correct): "Real-time inference":This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.

Option B:"Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low- latency needs.

Option C:"Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.

Option D:"Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.

AWS AI Practitioner References:

Amazon SageMaker Inference Options:AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

Correct Answer: B

Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of

accuracy. Option B (Correct): "Increase the epochs":This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.

Option A:"Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.

Option C:"Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy. Option D:"Increase the temperature parameter" is incorrect because temperature affects the

randomness of predictions, not model accuracy.

AWS AI Practitioner

References:

Model Training Best Practices on AWS:AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.