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NEW QUESTION: 1
You plan to deploy logical Azure SQL Database servers to The East US Azure region and the Wen US Azure region. Each server will contain 20 database accessed by a different user who reads in a different on premises location. The databases will be configured to use active geo-replication.
You need to recommend a solution that meets the following requirement!;
* Restricts user access to each database
* Restricts network access to each database based on each user's respective location
* Ensures that the databases remain accessible from down applications if the local Azure region fails What should you include in the recommendation? To answer, select the appropriate options m the answer area NOTE: Each correct selection is worth one point.

Answer:
Explanation:


NEW QUESTION: 2
Which of the following assertions is NOT true about pattern matching and anomaly detection in intrusion detection?
A. A pattern matching IDS can only identify known attacks
B. An anomaly-based engine develops baselines of normal traffic activity and throughput, and alerts on deviations from these baselines
C. Anomaly detection tends to produce more data
D. Stateful matching scans for attack signatures by analyzing individual packets instead of traffic streams
Answer: D
Explanation:
This is wrong which makes this the correct choice. This statement is not true as
stateful matching scans for attack signatures by analyzing traffic streams rather than individual
packets. Stateful matching intrusion detection takes pattern matching to the next level.
As networks become faster there is an emerging need for security analysis techniques that can
keep up with the increased network throughput. Existing network-based intrusion detection
sensors can barely keep up with bandwidths of a few hundred Mbps. Analysis tools that can deal
with higher throughput are unable to maintain state between different steps of an attack or they
are limited to the analysis of packet headers.
The following answers are all incorrect:
Anomaly detection tends to produce more data is true as an anomaly-based IDS produces a lot of
data as any activity outside of expected behavior is recorded.
A pattern matching IDS can only identify known attacks is true as a pattern matching IDS works by
comparing traffic streams against signatures. These signatures are created for known attacks.
An anomaly-based engine develops baselines of normal traffic activity and throughput, and alerts
on deviations from these baselines is true as the assertion is a characteristic of a statistical
anomaly-based IDS.
Reference:
Official guide to the CISSP CBK. Pages 198 to 201
http://cs.ucsb.edu/~vigna/publications/2003_vigna_robertson_kher_kemmerer_ACSAC03.pdf

NEW QUESTION: 3
一連のラベル付き写真を使用するマルチクラス画像分類の深層学習モデルがあります。次のコードを作成して、モデルをトレーニングするときにハイパーパラメーター値を選択します。

次の各ステートメントについて、ステートメントがtrueの場合は[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注:それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters