AWS Certified AI Practitioner AIF-C01 Syllabus Exam Questions Answers

Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.

Amazon SageMaker JumpStart:

Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.

Designed to simplify and accelerate the deployment of machine learning models, including foundation models.

Why Option B is Correct:

Rapid Deployment:JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.

Ease of Use:Provides a user-friendly interface to select and deploy models, reducing the time to value.

Why Other Options are Incorrect:

A. Amazon Personalize:Focuses on creating personalized recommendations, not deploying foundation models.

C. PartyRock:Not a recognized AWS service.

D. Amazon SageMaker endpoints:Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.

The temperature parameter in a large language model (LLM) controls the randomness of the model's output. A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.

Option A (Correct): "Decrease the temperature value":This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.

Option B:"Increase the temperature value" is incorrect because it would make the output more random and less consistent.

Option C:"Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.

Option D:"Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model’s responses.

AWS AI Practitioner References:

Understanding Temperature in Generative AI Models:AWS documentation explains that adjusting the temperature parameter affects the model’s output randomness, with lower values providing more consistent outputs.

Inference is the correct answer because it is the AI process that occurs when a deployed model analyzes new data (such as an image) to make predictions or identify objects.

Inference:

In the context of machine learning, inference is the process of using a trained model to make predictions on new, unseen data.

When the deep learning model is deployed to production and receives a new image for analysis, it uses the learned patterns from the training phase to identify objects in the image. This is known as inference.

Why Option B is Correct:

Inference Process:Involves applying the trained model to real-world data (the new image) to identify objects.

Deployment Context:The model has already been trained, and the deployment to production indicates it is being used for inference.

Why Other Options are Incorrect:

A. Training:Refers to the process of teaching the model using historical data, not making predictions on new data.

C. Model deployment:Refers to the process of making a trained model available for use in production.

D. Bias correction:Is a process to adjust a model to minimize bias, not for analyzing new images.

A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images.

Multi-Modal Embedding Model:

Can process and integrate different types of data (e.g., text and images) into a common representation space, enabling a unified search capability.

Suitable for applications where queries or content involve multiple data modalities.

Why Option A is Correct:

Handles Multiple Modalities:Supports both text and image data, aligning with the application's requirement.

Improves Search Relevance:Allows for more accurate and relevant search results across different types of input data.

Why Other Options are Incorrect:

B. Text embedding model:Only handles text data, not images.

C. Multi-modal generation model:Focuses on generating outputs rather than embedding for search tasks.

D. Image generation model:Only handles image data, not suitable for text queries.