Question 1

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?

Options :

A : Purchase Provisioned Throughput for the custom model.

B : Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

C : Register the model with the Amazon SageMaker Model Registry.

D : Grant access to the custom model in Amazon Bedrock.

Answer: B

Question 2

A retail company is developing a machine learning model to improve its customer segmentation and targeting strategies. The data science team is working with both labeled and unlabeled customer data to train their models but needs to clearly understand the distinction between these two types of data to decide how best to use them. Understanding this difference is critical for selecting the appropriate machine learning techniques, such as supervised or unsupervised learning, for different stages of the project.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 can only be used for unsupervised learning algorithms, whereas, unlabeled data is exclusively for supervised learning algorithmsLabeled 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

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 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

Answer: D

Question 3

A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication. Which solution meets these requirements?

Options :

A : Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.

B : Create medication review summaries by using Amazon Bedrock large language models (LLMs).

C : Create a classification model that categorizes medications into different groups by using Amazon SageMaker.

D : Create medication review summaries by using Amazon Rekognition.

Answer: B

Question 4

A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly. What should the company do to mitigate this problem?

Options :

A : Reduce the volume of data that is used in training.

B : Add hyperparameters to the model.

C : Increase the volume of data that is used in training.

D : Increase the model training time.

Answer: C

Question 5

An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms. What should the firm do when developing and deploying the LLM? (Select TWO.)

Options :

A : Include fairness metrics for model evaluation.

B : Adjust the temperature parameter of the model.

C : Modify the training data to mitigate bias.

D : Avoid overfitting on the training data.

E : Apply prompt engineering techniques.

Answer: A,C