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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each ques-tion on the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are migrating an on-premises application to Azure. One component of the application is a legacy Windows native executable that performs image processing.
The image processing application must run every hour. During times that the image processing application is not running, it should not be consuming any Azure compute resources.
You need to ensure that the image processing application runs correctly every hour.
Solution: Create an Azure Batch application that runs the image processing application every hour.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Use a VM to run the legacy application as a batch job.
The F-Series VMs are an excellent choice for gaming servers, web servers and batch processing.
Reference: https://azure.microsoft.com/en-us/blog/f-series-vm-size/

NEW QUESTION: 2
Which three selections are valid model numbers for Cisco ISE hardware appliances? (Choose three)
A. Cisco ISE 3355
B. Cisco ISE 3390
C. Cisco ISE 3310
D. Cisco ISE 3315
E. Cisco ISE 3395
F. Cisco ISE 3350
Answer: A,D,E
Explanation:
Section: (none)
Explanation/Reference:
Explanation:

NEW QUESTION: 3

A. Option B
B. Option A
C. Option D
D. Option C
Answer: B

NEW QUESTION: 4
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?
A. Apply the Pearson correlation coefficient
B. Perform one-hot encoding on highly correlated features
C. Use matrix multiplication on highly correlated features.
D. Create a new feature space using principal component analysis (PCA)
Answer: D