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NEW QUESTION: 1
A user has launched two EBS backed EC2 instances in the US-East-1a region. The user wants to change the zone of one of the instances. How can the user change it?
A. The zone can only be modified using the AWS CLI
B. Create an AMI of the running instance and launch the instance in a separate AZ
C. From the AWS EC2 console, select the Actions - > Change zones and specify new zone
D. Stop one of the instances and change the availability zone
Answer: B
Explanation:
With AWS EC2, when a user is launching an instance he can select the availability zone (AZ. at the time of launch. If the zone is not selected, AWS selects it on behalf of the user. Once the instance is launched, the user cannot change the zone of that instance unless he creates an AMI of that instance and launches a new instance from it.

NEW QUESTION: 2
A file is received daily from a data provider to the account SFTP. This file needs to be imported into Marketing Cloud as soon as it arrives. The arrival time of this file varies by several hours from day to day.
What tools should be used to accomplish this?
A. Automation Studio File Drop Starting Source
B. Automation Studio Schedule Starting Source
C. Journey Builder Fire Event Entry Source
D. Journey Builder Import Activity Entry Source
Answer: A

NEW QUESTION: 3
A large consumer goods manufacturer has the following products on sale:
- 34 different toothpaste variants
- 48 different toothbrush variants
- 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?
A. Train a custom ARIMA model to forecast demand for the new product.
B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
C. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
D. Train a custom XGBoost model to forecast demand for the new product.
Answer: C
Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

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