A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets What model should be used to complete this work?

1. A. K-means clustering
2. B. Random Cut Forest (RCF)
3. C. XGBoost
4. D. BlazingText

Correct Answer: A

While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

1. A. Dimensionality reduction
2. B. Data normalization
3. C. Model regulanzation
4. D. Data augmentation for the minority class

Correct Answer: D

A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

1. A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
2. B. Launch the notebook instances in a private subnet and access the data through a NAT gateway
3. C. Launch the notebook instances in a public subnet and access the data through a NAT gateway
4. D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

Correct Answer: D

A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

1. A. Load a smaller subset of the data into the SageMaker notebook and train locall
2. B. Confirm that the training code is executing and the model parameters seem reasonabl
3. C. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
4. D. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstanc
5. E. Train on a small amount of the data to verify the training code and hyperparameter
6. F. Go back toAmazon SageMaker and train using the full dataset
7. G. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMake
8. H. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.
9. I. Load a smaller subset of the data into the SageMaker notebook and train locall
10. J. Confirm that the training code is executing and the model parameters seem reasonabl
11. K. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Correct Answer: A

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?

1. A. Create a SageMaker endpoint and configuration for the new model versio
2. B. Redirect production traffic to the new endpoint by updating the client configuratio
3. C. Revert traffic to the last version if the model does not perform as expected.
4. D. Create a SageMaker endpoint and configuration for the new model versio
5. 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.
6. F. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new varian
7. G. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
8. 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