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

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

1. A. Specificity
2. B. False positive rate
3. C. Accuracy
4. D. Area under the precision-recall curve
5. E. True positive rate

Correct Answer: DE

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

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet this requirements?

1. A. Call the CreateNotebookInstanceLifecycleConfig API operation
2. B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
3. C. Stop and then restart the SageMaker notebook instance
4. D. Call the UpdateNotebookInstanceLifecycleConfig API operation

Correct Answer: C

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.
Which model describes the underlying data in this situation?

1. A. A naive Bayesian model, since the features are all conditionally independent.
2. B. A full Bayesian network, since the features are all conditionally independent.
3. C. A naive Bayesian model, since some of the features are statistically dependent.
4. D. A full Bayesian network, since some of the features are statistically dependent.

Correct Answer: C

A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

1. A. Add a VPC endpoint policy to allow access to the IAM users.
2. B. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
3. C. Modify the security group on the endpoint network interface to restrict access to the instances.
4. D. Modify the ACL on the endpoint network interface to restrict access to the instances.
5. E. Add a SageMaker Runtime VPC endpoint interface to the VPC.

Correct Answer: AC