Question 1

Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

Options :

A : Amazon Personalize

B : Amazon SageMaker JumpStart

C : PartyRock, an Amazon Bedrock Playground

D : Amazon SageMaker endpoints

Answer: B

Question 2

Which of the following use cases is addressed by the Amazon Forecast service?

Options :

A : Recommendations tailored to a user’s profile, behavior, preferences, and history

B : Predict product demand to accurately vary inventory and pricing at different store locations

C : Design conversational solutions that respond to frequently asked questions for technical support, and HR benefits

D : Detect and categorize toxic audio and foster a safe and inclusive online environment

Answer: B

Question 3

A retail company is exploring machine learning to enhance its customer segmentation and product recommendation systems. The data science team wants a solution that allows them to quickly get started with pre-built models and workflows, without needing extensive development from scratch. They are considering using Amazon SageMaker JumpStart to accelerate their machine learning projects, but they need to understand the platform's key features to ensure it meets their needs for rapid deployment and scalability.

Which of the following represent the key features of Amazon SageMaker JumpStart? (Select two)

Options :

A : You can build highly accurate ML models using a visual interface without any code

B : Your inference and training data will be used to train the base model

C : Pre-trained models are fully customizable for your use case with your data

D : SageMaker JumpStart provides only public models. Proprietary models are not supported by SageMaker JumpStart

Correct selection

E : You can evaluate, compare, and select Foundation Models quickly based on pre-defined quality and responsibility metrics

Answer: C,E

Question 4

What is a key difference between labeled data and unlabeled data in the context of machine learning?

Options :

A : Labeled data consists of raw information with no tags or annotations, while unlabeled data includes tags that provide meaning to the data

B : Labeled data is annotated with output labels that provide specific information about each data point and is used for supervised learning, whereas, unlabeled data lacks such annotations and is used for unsupervised learning

C : Labeled data is inherently more complex to process due to the lack of structure, whereas unlabeled data is easier to handle because it has clear annotations

D : Labeled data can only be used for unsupervised learning algorithms, whereas, unlabeled data is exclusively for supervised learning algorithms

Answer: B

Question 5

Which Integrated Development Environments (IDEs) are supported by Amazon SageMaker Studio for ML development?

Options :

A : Code Editor

B : RStudio

C : All

D : JupyterLab

Answer: C