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NEW QUESTION: 1
Which two are examples of information that would be displayed in the Activity pane fo the vSphere Web Client? (Choose two.)
A. The memory utilization of an ESXi host.
B. The current Fault Tolerance Status of a virtual machine.
C. A triggered alarm for an ESXi host.
D. The state of a vMotion for a virtual machine.
Answer: A,C

NEW QUESTION: 2
When an order was submitted via the corporate website, an administrator noted special characters (e.g., ";--" and "or
1=1 --") were input instead of the expected letters and numbers.
Which of the following is the MOST likely reason for the unusual results?
A. The user has been compromised by a cross-site scripting attack (XSS) and is part of a botnet performing DDoS attacks.
B. The user is attempting to fuzz the web server by entering foreign language characters which are incompatible with the website.
C. The user is attempting to highjack the web server session using an open-source browser.
D. The user is sending malicious SQL injection strings in order to extract sensitive company or customer data via the w ebsite.
Answer: D
Explanation:
The code in the question is an example of a SQL Injection attack. The code '1=1' will always provide a value of true.
This can be included in statement designed to return all rows in a SQL table.
SQL injection is a code injection technique, used to attack data-driven applications, in which malicious SQL statements are inserted into an entry field for execution (e.g. to dump the database contents to the attacker). SQL injection must exploit a security vulnerability in an application's software, for example, when user input is either incorrectly filtered for string literal escape characters embedded in SQL statements or user input is not strongly typed and unexpectedly executed. SQL injection is mostly known as an attack vector for websites but can be used to attack any type of SQL database.
Incorrect Answers:
A. Session hijacking, also known as TCP session hijacking, is a method of taking over a Web user session by surreptitiously obtaining the session ID and masquerading as the authorized user. Once the user's session ID has been accessed (through session prediction), the attacker can masquerade as that user and do anything the user is authorized to do on the network. The code in the question is not an example of session hijacking.
B. A distributed denial-of-service (DDoS) attack occurs when multiple systems flood the bandwidth or resources of a targeted system, usually one or more web servers. Such an attack is often the result of multiple compromised systems
(for example a botnet) flooding the targeted system with traffic. When a server is overloaded with connections, new connections can no longer be accepted. The major advantages to an attacker of using a distributed denial-of-service attack are that multiple machines can generate more attack traffic than one machine, multiple attack machines are harder to turn off than one attack machine, and that the behavior of each attack machine can be stealthier, making it harder to track and shut down. These attacker advantages cause challenges for defense mechanisms. For example, merely purchasing more incoming bandwidth than the current volume of the attack might not help, because the attacker might be able to simply add more attack machines. This after all will end up completely crashing a website for periods of time. The code in the question is not an example of the data that would be sent in a DDoS attack.
C. Fuzz testing or fuzzing is a software testing technique used to discover coding errors and security loopholes in software, operating systems or networks by inputting massive amounts of random data, called fuzz, to the system in an attempt to make it crash. The code in the question does not contain foreign language characters; it is code typically used in a SQL injection attack.
References:
http://en.wikipedia.org/wiki/SQL_injection
http://searchsoftwarequality.techtarget.com/definition/session-hijacking
http://en.wikipedia.org/wiki/Denial-of-service_attack

NEW QUESTION: 3
You are building a regression model tot estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE:
Each correct selection is worth one point.
A. The label data can be positive or negative,
B. The label data must be a positive value
C. The label data must be a negative value.
D. The data must be whole numbers.
E. The label data must be non discrete.
Answer: B,D
Explanation:
Explanation
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
* The response variable has a Poisson distribution.
* Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.
* A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression
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.

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