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NEW QUESTION: 1
DRAG DROP

Answer:
Explanation:

NEW QUESTION: 2
Refer to the exhibit.

Python threading allows a developer to have different parts of a program run concurrently and simplify a design. Drag and drop the code snippets from the left onto the item numbers on the right that match the missing sections in the exhibit to create a thread instance.

Answer:
Explanation:

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in 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, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an app named App1 that uses the Face API.
App1 contains several PersonGroup objects.
You discover that a PersonGroup object for an individual named Ben Smith cannot accept additional entries.
The PersonGroup object for Ben Smith contains 10,000 entries.
You need to ensure that additional entries can be added to the PersonGroup object for Ben Smith. The solution must ensure that Ben Smith can be identified by all the entries.
Solution: You migrate all the entries to the LargePersonGroup object for Ben Smith.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation
LargePersonGroup and LargeFaceList are collectively referred to as large-scale operations. LargePersonGroup can contain up to 1 million persons, each with a maximum of 248 faces. LargeFaceList can contain up to 1 million faces. The large-scale operations are similar to the conventional PersonGroup and FaceList but have some differences because of the new architecture.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-use-large-scale