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NEW QUESTION: 1
Which Citrix component will a Citrix Administrator install on every machine that delivers published applications?
A. Citrix Studio
B. StoreFront server
C. Virtual Delivery Agent (VDA)
D. Provisioning Services
Answer: A
NEW QUESTION: 2
-- Exhibit -
-- Exhibit -Click the Exhibit button. On R1, the interface fe-0/0/1 is assigned to the default routing instance and fe-0/0/2 is assigned to
a virtual router instance named VR-1.
Referring to the exhibit, the static route 200.200.200.200/32 is missing from the routing table of the
default routing instance.
What should you do on R1 to resolve this problem?
A. Configure an import policy to import the route 200.200.200.200/32 to the VR-1 instance.
B. Configure a static ARP entry for address 70.1.1.1.
C. Create a RIB group to import the direct route 70.1.1.0/24 to both the default instance and the VR-1 instance.
D. Change the instance type on VR-1 to forwarding.
Answer: C
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 XGBoost model to forecast demand for the new product.
B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
C. Train a custom ARIMA model to forecast demand for the new product.
D. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
Answer: B
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
NEW QUESTION: 4
A. Option C
B. Option D
C. Option A
D. Option B
Answer: D
Explanation:
Explanation
An Auto Scaling group is associated with one launch configuration at a time, and you can't modify a launch configuration after you've created it. To change the launch configuration for an Auto Scaling group, you can use an existing launch configuration as the basis for a new launch configuration and then update the Auto Scaling group to use the new launch configuration.
After you change the launch configuration for an Auto Scaling group, any new instances are launched using the new configuration options, but existing instances are not affected.
Then to ensure the new instances are launches, change the size of the Autoscaling Group to 6 and once the new instances are launched, change it back to 3.
For more information on instances scale-in process and Auto Scaling Group's termination policies please view the following link:
* https://docs
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