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NEW QUESTION: 1
What are three characteristics of the 802.11g standard? (Choose three.)
A. backward-compatibility with 802.11b
B. OFDM and CCK as additional modulation techniques
C. bacward-compatibility with 802.11a
D. speed of as much as 11 Mb/s
E. OFDM as an additional modulation technique
F. speed of as much as 54 Mb/s
Answer: A,E,F
Explanation:
Explanation/Reference:
Explanation:
802.11g is the third modulation standard for wireless LANs. It works in the 2.4 GHz band (like 802.11b) but operates at a maximum raw data rate of 54 Mbit/s. Using the CSMA/CA transmission scheme, 31.4 Mbit/s is the maximum net throughput possible for packets of 1500 bytes in size and a 54 Mbit/s wireless rate (identical to 802.11a core, except for some additional legacy overhead for backward compatibility). In practice, access points may not have an ideal implementation and may therefore not be able to achieve even 31.4 Mbit/s throughput with 1500 byte packets. 1500 bytes is the usual limit for packets on the Internet and therefore a relevant size to benchmark against. Smaller packets give even lower theoretical throughput, down to 3 Mbit/s using 54 Mbit/s rate and 64 byte packets. Also, the available throughput is shared between all stations transmitting, including the AP so both downstream and upstream traffic is limited to a shared total of 31.4 Mbit/s using 1500 byte packets and 54 Mbit/s rate. 802.11g hardware is fully backwards compatible with 802.11b hardware. Details of making b and g work well together occupied much of the lingering technical process. In an 802.11g network, however, the presence of a legacy
802.11b participant will significantly reduce the speed of the overall 802.11g network. Some 802.11g routers employ a back-compatible mode for 802.11b clients called 54g LRS (Limited Rate Support). [2] The modulation scheme used in 802.11g is orthogonal frequency-division multiplexing (OFDM) copied from 802.11a with data rates of 6, 9, 12, 18, 24, 36, 48, and 54 Mbit/s, and reverts to CCK (like the
802.11b standard) for 5.5 and 11 Mbit/s and DBPSK/DQPSK+DSSS for 1 and 2 Mbit/s. Even though
802.11g operates in the same frequency band as 802.11b, it can achieve higher data rates because of its heritage to 802.11a.
http://en.wikipedia.org/wiki/IEEE_802.11g-2003
NEW QUESTION: 2
You need to define a process for penalty event detection.
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.
Answer:
Explanation:
Topic 2, Case Study 2
Case study
Overview
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.
Datasets
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.
Dataset issues
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.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment requirements
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.
Model training
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.
Testing
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.
Data visualization
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: 3
Which application provides the opportunity to align security events with organizational controls, automatically appraising other business functions of potential impact?
A. Event Management
B. Service Mapping
C. Governance. Risk, and Compliance
D. Performance Analytics
Answer: C
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