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Please look through the features of them as follows.

NEW QUESTION: 1
Which two methods should you use to verify that an Oracle Management Cloud Gateway agent is installed, configured, and running?
A. Confirm that the Gateway has recently checked in on the Oracle Management Cloud Agents page.
B. Confirm that the Gateway appears within the Enterprise Overview dashboard of IT Analytics.
C. From the command line on the server the Gateway is installed on, runomcli statusagent.
D. Run theAgentInstall.shscript on the machine the Gateway is installed on.
E. Confirm that the Gateway can be viewed in the Oracle Cloud My Services dashboard.
F. From the command line on the server the Gateway is installed on, run theemctlstatus agent.
Answer: A,C

NEW QUESTION: 2
Azure Machine Learning Studioを使用して、バイナリ分類モデルを作成します。
モデルのパラメータースイープを実行して、ハイパーパラメーターを調整する必要があります。パラメータスイープは、次の要件を満たしている必要があります。
*ハイパーパラメーターの可能なすべての組み合わせを反復する
*スイープの実行に必要なコンピューティングリソースを最小限に抑える
*モデルのパラメータースイープを実行する必要があります。
どのパラメータースイープモードを使用する必要がありますか？
A. グリッド全体
B. ランダムグリッド
C. ランダムスイープ
D. スイープクラスタリング
E. ランダムシード
Answer: B
Explanation:
Explanation
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Topic 2, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
*Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
*Access a user's tendency to respond to an advertisement.
*Customize styles of ads served on mobile devices.
*Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
*Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1+/-5%.
* The ad propensity model uses cost factors shown in the following diagram:

The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

Penalty detection and sentiment
Findings
*Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
*Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
*Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
*Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
*Global penalty detection models must be trained by using dynamic runtime graph computation during training.
*Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
*Ad response rates declined.
*Drops were not consistent across ad styles.
*The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
*Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
*All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
*Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
*The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

NEW QUESTION: 3
For which reason would you configure RSPAN instead of SPAN on your network?
A. Only RSPAN can log traffic on a VLAN that spans multiple switches.
B. RSPAN provides more complete monitoring of the traffic on a single switch.
C. Only RSPAN devices can monitor traffic on other device.
D. Only RSPAN supports STP on multiple switches across a campus.
Answer: A

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