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NEW QUESTION: 1
Which two options are signaling protocols that are used in MPLS? (Choose two.)
A. LDP
B. CDP
C. BFD
D. LISP
E. RSVP
F. CLNS
Answer: A,E
Explanation:
Explanation/Reference:
Explanation:
* Signaling is the means by which LSRs all along the path know that they are a part of a given LSP. It is a signaling function by which the LSR knows that the internal transit path for the LSP depicted goes from Interface 2 to Interface 4.
* Label distribution is the means by which an LSR tells an upstream LSR what label value to use for a particular LSP.
There are four protocols that can perform the label distribution function:
* * * * Label Distribution Protocol (LDP)
Resource Reservation Protocol with Traffic Engineering Extensions (RSVP-TE) Constraint-Based Routed LDP (CR-LDP) Multiprotocol BGP LDP and RSVP-TE are the two most commonly used label distribution protocols Reference: http://www.networkworld.com/article/2237487/cisco-subnet/understanding-mpls-label- distribution.html
Topic 6, Infrastructure Services

NEW QUESTION: 2
All of the following are true regarding the Ishiwaka diagram in the quality planning process except:
A. They are also called as fish Bone diagram
B. They are an output of this process.
C. They are the tools and techniques
D. They are also called as cause-effect diagram
Answer: B
Explanation:
I agree with the answer. But I feel that the questions should be relating to Quality Control rather than Quality Planning. Because we don't use Fish bone diagram during the Quality planning process. Please let me know your comments on this. The PMBOK specifically
mentions Ishikawa diagrams as part of the quality planning process. I've never used them personally. From what I've read about them, they are a tool for organizing and visualizing causes of quality issues, used for brainstorming sessions and the like. As such, they would be a planning tool, rather than a quality control tool. Quality control analyzes specific quality issues to find and correct the root cause. Here's a good link: http://mot.vuse.vanderbilt.edu/mt322/Ishikawa.htm In terms of nomenclature, PMBOK's approach to quality seems to be: Quality Planning = develop quality management plan, design development processes to maximize quality Quality Assurance = use the quality management plan during project execution Quality Control = measure quality and respond to specific quality issues, including rework and updates to development processes

NEW QUESTION: 3
You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.
You need to format the data for the Weka environment.
Which module should you use?
A. Convert to ARFF
B. Convert to Dataset
C. Convert to CSV
D. Convert to SVMLight
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.

Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

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:

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