A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.
What are the key benefits of using Amazon Bedrock agents that could help this retailer?
Correct Answer:
B
Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.
✑ Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows": This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.
✑ Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.
✑ Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.
✑ Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.
AWS AI Practitioner References:
✑ Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
Correct Answer:
B
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.
✑ Amazon SageMaker Clarify:
✑ Why Option B is Correct:
✑ Why Other Options are Incorrect:
Thus, B is the correct answer for meeting transparency and explainability requirements for the foundation model
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?
Correct Answer:
B
Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.
✑ Option B (Correct): "Accuracy": This is the correct answer because accuracy
measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.
✑ Option A: "R-squared score" is incorrect as it is used for regression analysis, not
classification tasks.
✑ Option C: "Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.
✑ Option D: "Learning rate" is incorrect as it is a hyperparameter for training, not a performance metric.
AWS AI Practitioner References:
✑ Evaluating Machine Learning Models on AWS: AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
Correct Answer:
A
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:
✑ Why Option A is Correct:
✑ Why Other Options are Incorrect:
A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?
Correct Answer:
D
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost- effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
✑ Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
✑ Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-
intensive tasks but not specifically optimized for ML training with environmental considerations.
✑ Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is
optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.
✑ Option C: "Amazon EC2 P series" is designed for ML training but does not offer
the same level of energy efficiency as the Trn series.
AWS AI Practitioner References:
✑ AWS Trainium Overview: AWS promotes Trainium instances as their most energy- efficient and cost-effective solution for ML model training.
