Free AWS-Certified-Machine-Learning-Specialty Exam Braindumps

A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company’s data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucke
2. B. Create an ECS cluster that is based on an AWS Deep Learning Containers imag
3. C. Write the code to perform the feature engineerin
4. D. Train a logistic regression model for predicting the price, pointing to the bucket with the datase
5. E. Wait for the training job to complet
6. F. Perform the inferences.
7. G. Create an Amazon SageMaker notebook with a new IAM role that is associated with the noteboo
8. H. Pull the dataset from the S3 bucke
9. I. Explore different combinations of feature engineering transformations,regression algorithms, and hyperparameter
10. J. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
11. K. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambd
12. L. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the datase
13. M. Specify the price as the target featur
14. N. Wait for the job to complet
15. O. Load the model artifact to a Lambda function for inference on prices of new houses.
16. P. Create an IAM role for Amazon SageMaker with access to the S3 bucke
17. Q. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the datase
18. R. Specify the price as the target attribut
19. S. Wait for the job to complet
20. T. Deploy the best model for predictions.

Correct Answer: A

A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.
Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.
How can the company implement the testing model with the LEAST amount of operational overhead?

1. A. Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview featur
2. B. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.
3. C. Configure two SageMaker hosted endpoints that serve the different versions of the mode
4. D. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string paramete
5. E. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview featur
6. F. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version.
7. G. Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview featur
8. H. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.
9. I. Configure two SageMaker hosted endpoints that serve the different versions of the mode
10. J. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the mode
11. K. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other user
12. L. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version.

Correct Answer: D

A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

1. A. Linear regression
2. B. Classification
3. C. Clustering
4. D. Reinforcement learning

Correct Answer: B
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) – answers that need to be predicted – to train an algorithm. With classification, businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.
Which is the FASTEST route to index the assets?

1. A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
2. B. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
3. C. Use Amazon Transcribe to convert speech to tex
4. D. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
5. E. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.

Correct Answer: A

An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

1. A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
2. B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
3. C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
4. D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Correct Answer: C