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

Question 41

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
B. Use SCPs to restrict access to SageMaker.
C. Disable root access on the SageMaker notebook instances.
D. Enable network isolation for training jobs and models.
E. Restrict notebook presigned URLs to specific IPs used by the company.
F. Protect data with encryption at rest and in transi
G. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Correct Answer:BDE
https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine-learning-using-amaz

Question 42

A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

A. Decrease regularization.
B. Increase regularization.
C. Increase dropout.
D. Decrease dropout.
E. Increase feature combinations.
F. Decrease feature combinations.

Correct Answer:BCD

Question 43

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?

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
B. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
C. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatc
D. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
E. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrai
F. Add code to push a custom metric to Amazon CloudWatc
G. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
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

Question 44

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago
Which method should the Specialist try to improve model performance?

A. The model needs to be completely re-engineered because it is unable to handle product inventory changes
B. The model's hyperparameters should be periodically updated to prevent drift
C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
D. The model should be periodically retrained using the original training data plus new data as product inventory changes

Correct Answer:D

Question 45

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.
AWS-Certified-Machine-Learning-Specialty dumps exhibit
Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

A. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Correct Answer:DE