A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.
How should the data scientist accomplish this?

1. A. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the AmazonSageMaker notebook ARN as principal.
2. B. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.
3. C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.
4. D. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

Correct Answer: C

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

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s)
Which visualization will accomplish this?

1. A. A histogram showing whether the most important input feature is Gaussian.
2. B. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
3. C. A scatter plot showing (he performance of the objective metric over each training iteration
4. D. A scatter plot showing the correlation between maximum tree depth and the objective metric.

Correct Answer: D

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?

1. A. Master node
2. B. Any of the core nodes
3. C. Any of the task nodes
4. D. Both core and task nodes

Correct Answer: A

A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet.
Which set of actions should the data science team take to fix the issue?

1. A. Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VP
2. B. Apply this security group to all of the notebook instances' VPC interfaces.
3. C. Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoint
4. D. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.
5. E. Add a NAT gateway to the VP
6. F. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnet
7. G. Stop and start all of the notebook instances to reassign only private IP addresses.
8. H. Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC.

Correct Answer: B