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

A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.
A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.
What is the MOST direct approach to solve this problem within 2 days?

1. A. Train a custom classifier by using Amazon Comprehend.
2. B. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
3. C. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
4. D. Use a built-in seq2seq model in Amazon SageMaker.

Correct Answer: B

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.)

1. A. Decrease regularization.
2. B. Increase regularization.
3. C. Increase dropout.
4. D. Decrease dropout.
5. E. Increase feature combinations.
6. F. Decrease feature combinations.

Correct Answer: BCD

A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

1. A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.
2. B. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.
3. C. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
4. D. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

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 thetraining 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 compatible with Amazon SageMake
8. H. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe 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