Exam Code: 300-810
Exam Name: Implementing Cisco Collaboration Applications
Certification: CCNP Collaboration
Total Questions: 137 Q&A
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NEW QUESTION: 1
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
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.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
Topic 2, Two Case study
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:
The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
NEW QUESTION: 2
조직의 시설 내에있는 터미널이나 워크 스테이션을 통한 허가되지 않은 시스템 액세스의 가능성은 다음과 같은 경우에 증가합니다.
A. 랩탑을 네트워크에 연결할 수있는 시설에서 연결 지점을 사용할 수 있습니다.
B. 암호로 보호 된 터미널은 안전하지 않은 위치에 있습니다.
C. 터미널은 관리자의 감독하에 소규모 클러스터로 시설 내에 위치합니다.
D. 사용자는 암호를 기밀로 유지하기 위해 예방 조치를 취합니다.
부적절한 의도를 가진 사람은 노트북을 네트워크에 연결할 수 있습니다. 개인이 유효한 사용자 ID 및 암호에 대한 지식이있는 경우 보안되지 않은 액세스 지점을 사용할 수 없습니다. 다른 선택은 승인되지 않은 네트워크 액세스를 방지하기위한 제어입니다. 침입자가 시스템 암호를 쉽게 사용할 수없는 경우 추측 해 추가 요소를 도입하고 시간이 필요합니다. 시스템 암호는 안전하지 않은 위치에있는 터미널의 무단 사용을 방지합니다. 감독은 작은 운영 단위 또는 생산 자원에 대한 액세스를 모니터링하는 데 사용될 때 매우 효과적인 제어입니다.
NEW QUESTION: 3
NEW QUESTION: 4
A. EC2-Classicでは、インスタンスを最大500個のセキュリティグループに関連付け、セキュリティグループに最大100個のルールを追加できます。 EC2-VPCでは、ネットワークインターフェースを最大5つのセキュリティグループに関連付け、最大50のルールをセキュリティグループに追加できます。
B. EC2-Classicでは、インスタンスを最大5つのセキュリティグループに関連付け、最大50のルールをセキュリティグループに追加できます。 EC2-VPCでは、ネットワークインターフェースを最大500個のセキュリティグループに関連付け、セキュリティグループに最大100個のルールを追加できます。
C. EC2-Classicでは、インスタンスを最大500個のセキュリティグループに関連付けて、最大50個のルールをセキュリティグループに追加できます。 EC2-VPCでは、ネットワークインターフェースを最大5つのセキュリティグループに関連付け、セキュリティグループに最大100のルールを追加できます。
D. EC2-Classicでは、インスタンスを最大5つのセキュリティグループに関連付け、セキュリティグループに最大100のルールを追加できます。 EC2-VPCでは、ネットワークインターフェイスを最大500個のセキュリティグループに関連付けて、最大50個のルールをセキュリティグループに追加できます。
A security group acts as a virtual firewall that controls the traffic for one or more instances. When you launch an instance, you associate one or more security groups with the instance. You add rules to each security group that allow traffic to or from its associated instances. If you're using EC2-Classic, you must use security groups created specifically for EC2-Classic. In EC2-Classic, you can associate an instance with up to 500 security groups and add up to 100 rules to a security group. If you're using EC2-VPC, you must use security groups created specifically for your VPC. In EC2-VPC, you can associate a network interface with up to 5 security groups and add up to 50 rules to a security group.