AWS-Certified-Machine-Learning-Specialty Free Practice Test

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 Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

1. A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
2. B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
3. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
4. D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Correct Answer: B
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

1. A. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatc
2. B. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
3. C. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatc
4. D. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
5. E. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrai
6. F. Add code to push a custom metric to Amazon CloudWatc
7. G. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
8. H. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Correct Answer: C

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent
How should the Specialist frame this business problem'?

1. A. Streaming classification
2. B. Binary classification
3. C. Multi-category classification
4. D. Regression classification

Correct Answer: A

A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs
What does the Specialist need to do1?

1. A. Bundle the NVIDIA drivers with the Docker image
2. B. Build the Docker container to be NVIDIA-Docker compatible
3. C. Organize the Docker container's file structure to execute on GPU instances.
4. D. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

Correct Answer: A