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NEW QUESTION: 1
Sie haben ein Azure Migrate-Projekt mit den folgenden Bewertungseigenschaften:
* Zielort: Osten der USA
* Speicherredundanz: Lokal redundant
* Wohlfühlfaktor: 2,0
* Leistungsverlauf: 1 Monat
* Perzentilauslastung: 95
* Preisstufe: Standard
* Angebot: Bezahlen Sie, während Sie gehen
Sie entdecken die folgenden zwei virtuellen Maschinen:
* Eine virtuelle Maschine mit dem Namen VM1, auf der Windows Server 2016 ausgeführt wird und die 10 CPU-Kerne bei 20 Prozent Auslastung aufweist
* Eine virtuelle Maschine mit dem Namen VM2, auf der Windows Server 2012 ausgeführt wird und die vier CPU-Kerne mit einer Auslastung von 50 Prozent aufweist. Wie viele CPU-Kerne empfiehlt Azure Migrate für jede virtuelle Maschine? Wählen Sie zum Beantworten die entsprechenden Optionen im Antwortbereich aus.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.

Answer:
Explanation:

Erläuterung

Die Gleichung lautet: 'Kernnutzung x Komfortfaktor'. Der Wohlfühlfaktor beträgt 2,0.
VM 1 ist also 10 Kerne bei 20% Auslastung, was 2 Kernen entspricht. Multiplizieren Sie den Komfortfaktor und Sie erhalten 4 Kerne.
VM 2 besteht aus 4 Kernen bei 50% Auslastung, was 2 Kernen entspricht. Multiplizieren Sie den Komfortfaktor und Sie erhalten 4 Kerne.

NEW QUESTION: 2
Refer to the exhibit.

A router reserved these five routes from different routing information sources.
Which two routes does the router install in its routing table? (Choose two)
A. RIP route 10.0.0.0/30
B. EIGRP route 10.0.0.1/32
C. OSPF route 10.0.0.0/16
D. OSPF route 10.0.0.0/30
E. iBGP route 10.0.0.0/30
Answer: B,D

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) Incorrect Answers: ..SGD..
Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow.
Box 4: .. nn.parallel.DistributedDataParallel..
DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.
References:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py

NEW QUESTION: 4
You are adding a sub-grid to the Account form that displays data from a custom entity related to the account.
You need to identify which items you can use to display the data.
Which two items should you identify? Each correct answer presents a complete solution.
A. an iFrame
B. a chart
C. a dashboard
D. a fist
Answer: B,D