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NEW QUESTION: 1
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?
A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
D. Load a smaller subset of the data into the SageMaker notebook and tram locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
Answer: D
NEW QUESTION: 2
How do you prevent duplicate vendors from being created? (Choose two)
A. Run the Vendor De-Duplication report.
B. Activate master data comparison for the vendor.
C. Activate message control for the duplicate vendor check.
D. Use matchcode to check whether the vendor already exists.
Answer: C,D
NEW QUESTION: 3
300 야드 셔틀을 관리 할 때 평행선이 얼마나 멀리 떨어져 있습니까?
A. 75 야드
B. 50 야드
C. 100 야드
D. 25 야드
Answer: D
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