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NEW QUESTION: 1
グローバルペナルティ検出モデルのサンプリング戦略を構築するには、Python言語を使用する必要があります。
コードセグメントをどのように完成させる必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

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)

NEW QUESTION: 2
Regarding the process described above, what is the most important recommendation you would make using IEEE 1044 as a guide? 2 credits
A. No mentioning of reproduceability
B. Incident numbering is manual rather than automated
C. No priority or severity assigned
D. No classification on type of incident
Answer: C

NEW QUESTION: 3
You plan to store 20 TB of data in Azure. The data will be accessed infrequently and visualized by using
Microsoft Power BI.
You need to recommend a storage solution for the data.
Which two solutions should you recommend? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Azure Data Lake
B. Azure SQL Database
C. Azure Cosmos DB
D. Azure SQL Data Warehouse
E. Azure Database for PostgreSQL
Answer: A,D

NEW QUESTION: 4
You create a virtual machine scale set named Scale1. Scale1 is configured as shown in the following exhibit.
The subscription contains the Azure SQL databases shown in the following table.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1:
The Autoscale scale out rule increases the number of VMs by 2 if the CPU threshold is 80% or higher. The initial instance count is 4 and rises to 6 when the 2 extra instances of VMs are added.
Box 2:
The Autoscale scale in rule decreases the number of VMs by 4 if the CPU threshold is 30% or lower. The initial instance count is 4 and thus cannot be reduced to 0 as the minimum instances is set to 2. Instances are only added when the CPU threshold reaches 80%.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-overview
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-best-practices
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-common-scale-patterns