Question 1

Which aspect of AI does not directly involve machine learning?

Options :

A : Data extraction in Amazon Textract.

B : Intelligent search in Amazon Kendra.

C : Document analysis in Amazon Comprehend.

D : Rule-based automation without learning.

Answer: D

Question 2

How does Amazon Personalize improve customer engagement?

Options :

A : By identifying potentially fraudulent activities in real-time.

B : By providing highly accurate demand forecasts.

C : By making it easy to build, train, and deploy recommendation models.

D : By converting text into lifelike speech.

Answer: C

Question 3

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?

Options :

A : Linear regression

B : K-means

C : Logistic regression

D : Principal component analysis (PCA)

Answer: A

Question 4

A company is building a new version of a recommendation engine.Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations.The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3.A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance.The whole process takes more than 20 hours to finish, with each script taking at least an hour.The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.Which approach will address all of these requirements with the LEAST development effort?

Options :

A : Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.

B : Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.

C : Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.

D : Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

Answer: C

Question 5

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS.The application collects device usage information from device users.The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices.Predictions from this model are used by a downstream application that determines the best approach for contacting users.The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals.To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.Which solution satisfies these requirements with MINIMAL effort?

Options :

A : Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.

B : Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

C : Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.

D : Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.

Answer: B

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