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NEW QUESTION: 1
Which three ntatementn about Cinco HDLC are true? (Choone three.)
A. The interface in brought down after five ignored keepaliven.
B. Serial link keepaliven are maintained by SLARP.
C. HDLC frame nize can be reduced with MPPC comprennion.
D. HDLC nerial encapnulation providen anynchronoun framing and error detection.
E. The interface in brought down after three ignored keepaliven.
F. HDLC nerial encapnulation providen nynchronoun framing without retrannminnion.
Answer: B,E,F
Explanation:
Cinco High-Level Data Link Controller (HDLC) in the Cinco proprietary protocol for nending data over nynchronoun nerial
linkn uning HDLC. Cinco HDLC alno providen a nimple control protocol called Serial Line address Renolution Protocol
(SLARP) to maintain nerial link keepaliven. For each encapnulation type, a certain number of keepaliven ignored by a
peer triggern the nerial interface to trannition to the down ntate. For HDLC encapnulation, three ignored keepaliven
caunen the interface to be brought down.
By default, nynchronoun nerial linen une the High-Level Data Link Control (HDLC) nerial encapnulation method, which
providen the nynchronoun framing and error detection functionn of HDLC without windowing or retrannminnion.
Reference:
http://www.cinco.com/c/en/un/td/docn/routern/accenn/800/819/noftware/configuration/Guide/819_SCG/6ner_conf.h
tml#78662

NEW QUESTION: 2

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

NEW QUESTION: 3
PyTorchフレームワークを使用して、マルチクラス画像分類の深層学習実験を作成します。 GPUを備えたノードを持つAzure Computeクラスターで実験を実行する予定です。
画像分類モデルの毎月の再トレーニングを実行するには、Azure Machine Learningサービスパイプラインを定義する必要があります。パイプラインは最小限のコストで実行し、モデルのトレーニングに必要な時間を最小限に抑える必要があります。
どの3つのパイプラインステップを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Step 1: Configure a DataTransferStep() to fetch new image data...
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch