Question 1

What is a key difference between Amazon Bedrock and Amazon SageMaker JumpStart?

Options :

A : Amazon Bedrock provides foundational models for generative AI applications, whereas Amazon SageMaker JumpStart offers pre-built solutions and one-click deployment for various machine learning models

B : Amazon SageMaker JumpStart provides foundational models for generative AI applications, whereas Amazon Bedrock offers pre-built solutions and one-click deployment for various machine learning models

C : Amazon SageMaker JumpStart is designed for building and scaling machine learning models, whereas Amazon Bedrock is used for real-time data analytics

D : Amazon Bedrock is designed for building and scaling machine learning models, whereas Amazon SageMaker JumpStart is used for real-time data analytics

Answer: A

Question 2

A financial services company is deploying a machine learning model using Amazon Bedrock to predict loan approval risks. The data science team needs to ensure that the model performs effectively before going into production. They are focused on understanding the correct practices and tools for model evaluation on Amazon Bedrock to ensure accuracy, fairness, and reliability in their predictions.

Which of the following are correct regarding model evaluation for Amazon Bedrock? (Select two)

Options :

A : For human model evaluation, you can use either built-in prompt datasets or your own prompt datasets

B : Automatic model evaluation is valuable for assessing qualitative aspects of the model, whereas, human model valuation is valuable for assessing quantitative aspects of the model

C : Automatic model evaluation provides model scores that are calculated using various statistical methods such as BERT Score and F1

D : Human model evaluation provides model scores that are calculated using various statistical methods such as BERT Score and F1

E : Human model evaluation is valuable for assessing qualitative aspects of the model, whereas, automatic model valuation is valuable for assessing quantitative aspects of the model

Answer: C,E

Question 3

A company has developed an app that translates text from English to various other languages. To enhance the quality and accuracy of these translations, the company seeks to evaluate and improve its translation model's performance.

Which metric would be most appropriate for assessing the accuracy of the translations?

Options :

A : The company should use ROUGE (Recall-Oriented Understudy for Gisting Evaluation), a metric primarily used for evaluating the quality of text summarization and comparing the overlap between predicted and reference summaries

B : The company should use BLEU (Bilingual Evaluation Understudy) score, a metric specifically designed to evaluate the quality of text that has been machine-translated by comparing it with one or more reference translations

C : The company should use BERT score, a metric that leverages contextual embeddings from BERT to evaluate the semantic similarity between machine-generated translations and reference translations

D : The company should use Accuracy, a general metric that measures the proportion of correctly predicted outputs

Answer: B

Question 4

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 5

What is a key difference between reinforcement learning and supervised learning?

Options :

A : Reinforcement learning focuses on an agent learning optimal actions through interactions with the environment and feedback, while supervised learning involves training models on labeled data to make predictions

B : Reinforcement learning uses unlabeled data to cluster data points, whereas supervised learning uses labeled data to make predictions

C : Reinforcement learning and supervised learning both require labeled datasets for training models

D : Reinforcement learning relies on learning from labeled datasets, whereas supervised learning involves an agent taking actions to receive rewards or penalties

Answer: A

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