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NEW QUESTION: 1
3DESと比べてAESの利点は2つありますか。
（2つ選んでください。）
A. 暗号化ブロック長が長くなります
B. 短い暗号化キー
C. 高速暗号化
D. 32 GBのデータ転送ごとに暗号化キーを切り替えます
E. 暗号化キーの繰り返し
Answer: A,C
NEW QUESTION: 2
DRAG DROP
Drop
Answer:
Explanation:
Explanation:
1) T1 or E1 with CAS or PRI: PBX to PBX2) FXO: off-net3) FXS: local4) FXS or
switcH. on-net5) E&M, FXO, FXS: PLAR
Explanation
PBX to PBX connections can use T1 or E1 with CAS or PRI: PBX can connect to a network
through T1 or E1 lines with channel associated signaling (CAS) or Primary Rate Interface (PRI) signaling.
For off-net calls, the typical connection between the router and the PSTN is through FXO port.
A local call just needs FXS ports so it is the only choice for this type of call.
We can make on-net calls through FXS port (phone directly connected to the router) or FXO port
(phone connected to a PBX). The "switch" here means that we can connect an IP phone through a
switch and place on-net calls through Cisco Unified Communications Manager.
A PLAR call can work with any type of signaling, including E&M, FXO, FXS interfaces.
NEW QUESTION: 3
You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.
You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:
You have the following script.
You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.
Which code should you use to configure the estimator?
A. Option E
B. Option A
C. Option D
D. Option B
E. Option C
Answer: D
Explanation:
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Incorrect Answers:
A: Pandas DataFrame not used.
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets
NEW QUESTION: 4
A SAP-ingress policy is configured so that ping traffic is classified as FC L1 and mapped to queue 3; however, all traffic is placed in queue 1. What is the most probable reason for this?
A. The SAP-ingress policy has not been applied to the SAP.
B. The default forwarding class for ICMP traffic is BE and cannot be changed.
C. Queue 3 has no PIR or CIR configured, and thus cannot hold any packets.
D. The SAP has been shut down.
Answer: A