Question 1

How can you prevent model-overfitting in machine learning?

Options :

A : By increasing the complexity of the model to ensure it captures all nuances in the training data

B : By only training the model on a small subset of the available data to reduce the amount of information it has to learn

C : By using techniques such as cross-validation, regularization, and pruning to simplify the model and improve its generalization

D : By avoiding any form of model validation or testing to prevent the model from learning incorrect patterns

Answer: C

Question 2

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

Question 3

Which of the following is mandatory when setting autotune configuration for Amazon SageMaker Automatic Model Tuning (AMT) ?

Options :

A : None

B : Number of jobs

C : Hyperparameter ranges

D : Tuning strategy

Answer: A

Question 4

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?

Options :

A : Generation of custom foundation models (FMs) to predict customer needs

B : Automation of repetitive tasks and orchestration of complex workflows

C : Automatically calling multiple foundation models (FMs) and consolidating the results

D : Selecting the foundation model (FM) based on predefined criteria and metrics

Answer: B

Question 5

Which of the following summarizes the capabilities of a multimodal model?

Options :

A : A multimodal model can accept a mix of input types such as audio/text and create a mix of output types such as video/image

B : A multimodal model can accept a mix of input types such as audio/text, however, it can only create a single type of output

C : A multimodal model can accept only a single type of input and it can only create a single type of output

D : A multimodal model can accept only a single type of input, however, it can create a mix of output types such as video/image

Answer: A