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NEW QUESTION: 1
A. Option B
B. Option C
C. Option A
D. Option D
Answer: A
NEW QUESTION: 2
A developer has modified the out-of-the-box DAM Update Asset workflow to include a Review & Approval Process Step that generates a task whenever a new video asset is created or when a videoasset is modified.
However whenthe developer uploaded a new video to the DAM and has approved the video asset it suddenly generated another Approval Task after completing the previous one.
The workflow launcher for both the Create and Modified events have been configured with the following:
What are thetwo things you can change to fixthis issue? (Choose two.)
A. Asset Created event Add the dc:format==video/- condition
B. Asset Modified event Add the jcr:content/jcr:mimeType= =video/.' condition
C. Add the dam status property in the exclude list of the DAM Update Asset Modified event
D. Add the dam status property in the exclude list of the DAM Update Asset Created event
Answer: A,B
NEW QUESTION: 3
You need to use the Python language to build a sampling strategy for the global penalty detection models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: import pytorch as deeplearninglib
Box 2: ..DistributedSampler(Sampler)..
DistributedSampler(Sampler):
Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.
Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.
Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10)