Question 1

A Machine Learning Specialist is preparing data for training on Amazon SageMaker.The Specialist is using one of the SageMaker built-in algorithms for the training.The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.What should the Specialist do to optimize the data for training on SageMaker?

Options :

A : Use AWS Glue to compress the data into the Apache Parquet format.

B : Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

C : Transform the dataset into the RecordIO protobuf format.

D : Use the SageMaker batch transform feature to transform the training data into a DataFrame.

Answer: C

Question 2

What is the primary benefit of Amazon Forecast for users without ML experience?

Options :

A : Requires no ML experience to get started.

B : Provides advanced visualization tools for data analysis.

C : Integrates seamlessly with all Amazon products.

D : Automatically generates forecasts based on real-time data.

Answer: A

Question 3

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

Question 4

What role will machine learning play in the future of technology?

Options :

A : It will only be used in healthcare

B : It will be boundless with applications in various fields

C : It will become obsolete

D : It will be limited to fraud detection

Answer: B

Question 5

What is an indication that ML might be an overcomplicated solution?

Options :

A : The problem involves generating personalized responses.

B : The problem can be solved with traditional algorithms.

C : The problem involves complex and dynamic data.

D : The problem requires quick adaptation to new data.

Answer: B