AWS-Certified-Machine-Learning-Specialty Amazon Exam Questions and Free Practice Test

Question 13

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.)

A. Add a VPC endpoint policy to allow access to the IAM users.

B. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.

C. Modify the security group on the endpoint network interface to restrict access to the instances.

D. Modify the ACL on the endpoint network interface to restrict access to the instances.

E. Add a SageMaker Runtime VPC endpoint interface to the VPC.

Correct Answer:AC

Question 14

An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend

B. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

C. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

Correct Answer:A

Question 15

A company offers an online shopping service to its customers. The company wants to enhance the site’s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.
The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.
Which approach should an ML specialist take to implement the new security feature in the web application?

A. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attemp

B. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.

C. Use Amazon SageMaker to train a model using the IP Insights algorith

D. Schedule updates and retraining of the model using new log data nightly.

E. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attemp

F. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

G. Use Amazon SageMaker to train a model using the Object2Vec algorith

H. Schedule updates and retraining of the model using new log data nightly.

Correct Answer:C

Question 16

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?

A. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file syste

B. Run the SageMaker training job with the FSx for Lustre file system as the data source.

C. Launch a transient Amazon EMR cluste

D. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistC

E. Run the SageMaker training job with Amazon S3 as the data source.

F. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucke

G. Run the SageMaker training job with Amazon S3 as the data source.

H. Run a SageMaker training job with an EFS file system as the data source.

Correct Answer:A

Question 17

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

A. Run self-correlation on all features and remove highly correlated features

B. Normalize all numerical values to be between 0 and 1

C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features

D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Correct Answer:B

Question 18

A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed
What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

A. Create a SageMaker endpoint and configuration for the new model versio

B. Redirect production traffic to the new endpoint by updating the client configuratio

C. Revert traffic to the last version if the model does not perform as expected.

D. Create a SageMaker endpoint and configuration for the new model versio

E. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.

F. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new varian

G. Revert traffic to the last version by resetting the weights if the model does not perform as expected.

H. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Correct Answer:A

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